diff --git a/.gitignore b/.gitignore index 6606d1085..0f73f4303 100644 --- a/.gitignore +++ b/.gitignore @@ -158,4 +158,8 @@ dmypy.json **/log/ # Mac OS -.DS_Store \ No newline at end of file +.DS_Store + +results +eeg_data +trial_*.conf \ No newline at end of file diff --git a/benchmarks/MOABB/extra-requirements.txt b/benchmarks/MOABB/extra-requirements.txt index 21f8c3838..90e861602 100644 --- a/benchmarks/MOABB/extra-requirements.txt +++ b/benchmarks/MOABB/extra-requirements.txt @@ -1,6 +1,7 @@ -mne==1.6.1 +mne moabb orion orion[profet] scikit-learn +torch_geometric torchinfo diff --git a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml new file mode 100644 index 000000000..72ceb336d --- /dev/null +++ b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml @@ -0,0 +1,187 @@ +seed: 1234 +__set_torchseed: !apply:torch.manual_seed [!ref ] + +# DIRECTORIES +data_folder: + !PLACEHOLDER #'/path/to/dataset'. The dataset will be automatically downloaded in this folder + + +cached_data_folder: !PLACEHOLDER #'path/to/pickled/dataset' + + +output_folder: !PLACEHOLDER + #'path/to/results' + + # DATASET HPARS + # Defining the MOABB dataset. + + +datasets: + - !new:moabb.datasets.BNCI2014001 +save_prepared_dataset: True # set to True if you want to save the prepared dataset as a pkl file to load and use afterwards +data_iterator_name: !PLACEHOLDER +target_subject_idx: !PLACEHOLDER +target_session_idx: !PLACEHOLDER +events_to_load: null # all events will be loaded +original_sample_rate: 250 # Original sampling rate provided by dataset authors +sample_rate: 125 # Target sampling rate (Hz) +# band-pass filtering cut-off frequencies +fmin: 0.13 # orion_step1: --fmin~"uniform(0.1, 5, precision=2)" +fmax: 46.0 # @orion_step1: --fmax~"uniform(20.0, 50.0, precision=3)" +n_classes: 4 +# tmin, tmax respect to stimulus onset that define the interval attribute of the dataset class +# trial begins (0 s), cue (2 s, 1.25 s long); each trial is 6 s long +# dataset interval starts from 2 +# -->tmin tmax are referred to this start value (e.g., tmin=0.5 corresponds to 2.5 s) +tmin: 0. +tmax: 4.0 # @orion_step1: --tmax~"uniform(1.0, 4.0, precision=2)" +n_steps_channel_selection: null +T: !apply:math.ceil + - !ref * ( - ) +C: 22 +# We here specify how to perfom test: +# - If test_with: 'last' we perform test with the latest model. +# - if test_with: 'best, we perform test with the best model (according to the metric specified in test_key) +# The variable avg_models can be used to average the parameters of the last (or best) N saved models before testing. +# This can have a regularization effect. If avg_models: 1, the last (or best) model is used directly. +test_with: "last" # 'last' or 'best' +test_key: "acc" # Possible opts: "loss", "f1", "auc", "acc" + +# METRICS +f1: !name:sklearn.metrics.f1_score + average: "macro" +acc: !name:sklearn.metrics.balanced_accuracy_score +cm: !name:sklearn.metrics.confusion_matrix +metrics: + f1: !ref + acc: !ref + cm: !ref +# TRAINING HPARS +n_train_examples: 100 # it will be replaced in the train script +# checkpoints to average +avg_models: 10 # @orion_step1: --avg_models~"uniform(1, 15,discrete=True)" +number_of_epochs: 862 # @orion_step1: --number_of_epochs~"uniform(500, 1000, discrete=True)" +lr: 0.0001 # orion_step1: --lr~"choices([0.01, 0.005, 0.001, 0.0005, 0.0001])" +# Learning rate scheduling (cyclic learning rate is used here) +max_lr: !ref # Upper bound of the cycle (max value of the lr) +base_lr: 0.00000001 # Lower bound in the cycle (min value of the lr) +step_size_multiplier: 5 #from 2 to 8 +step_size: !apply:round + - !ref * / +lr_annealing: !new:speechbrain.nnet.schedulers.CyclicLRScheduler + base_lr: !ref + max_lr: !ref + step_size: !ref +label_smoothing: 0.0 +loss: !name:speechbrain.nnet.losses.nll_loss + label_smoothing: !ref +optimizer: !name:torch.optim.Adam + lr: !ref +epoch_counter: !new:speechbrain.utils.epoch_loop.EpochCounter # epoch counter + limit: !ref +batch_size_exponent: 4 # @orion_step1: --batch_size_exponent~"uniform(4, 6,discrete=True)" +batch_size: !ref 2 ** +valid_ratio: 0.2 + +# DATA AUGMENTATION +# cutcat (disabled when min_num_segments=max_num_segments=1) +max_num_segments: 3 # orion_step2: --max_num_segments~"uniform(2, 6, discrete=True)" +cutcat: !new:speechbrain.augment.time_domain.CutCat + min_num_segments: 2 + max_num_segments: !ref +# random amplitude gain between 0.5-1.5 uV (disabled when amp_delta=0.) +amp_delta: 0.01742 # orion_step2: --amp_delta~"uniform(0.0, 0.5)" +rand_amp: !new:speechbrain.augment.time_domain.RandAmp + amp_low: !ref 1 - + amp_high: !ref 1 + +# random shifts between -300 ms to 300 ms (disabled when shift_delta=0.) +shift_delta_: 1 # orion_step2: --shift_delta_~"uniform(0, 25, discrete=True)" +shift_delta: !ref 1e-2 * # 0.250 # 0.-0.25 with steps of 0.01 +min_shift: !apply:math.floor + - !ref 0 - * +max_shift: !apply:math.floor + - !ref 0 + * +time_shift: !new:speechbrain.augment.freq_domain.RandomShift + min_shift: !ref + max_shift: !ref + dim: 1 +# injection of gaussian white noise +snr_white_low: 15.0 # orion_step2: --snr_white_low~"uniform(0.0, 15, precision=2)" +snr_white_delta: 19.1 # orion_step2: --snr_white_delta~"uniform(5.0, 20.0, precision=3)" +snr_white_high: !ref + +add_noise_white: !new:speechbrain.augment.time_domain.AddNoise + snr_low: !ref + snr_high: !ref +position_noise_sigma: 0.01 # @orion_step1: --position_noise_sigma~"uniform(0.0, 0.1, precision=4)" + +repeat_augment: 1 # @orion_step1: --repeat_augment 0 +graph_augment: !new:torch.nn.Sequential + - !new:utils.graph_iterators.PositionNoise + sigma: !ref + - !new:utils.graph_iterators.NodeDrop + choose_k: 17 +augment: !new:speechbrain.augment.augmenter.Augmenter + parallel_augment: True + concat_original: True + parallel_augment_fixed_bs: True + repeat_augment: !ref + shuffle_augmentations: True + min_augmentations: 4 + max_augmentations: 4 + augmentations: + [!ref , !ref , !ref , !ref ] + +# DATA NORMALIZATION +dims_to_normalize: 1 # 1 (time) or 2 (EEG channels) +normalize: !name:speechbrain.processing.signal_processing.mean_std_norm + dims: !ref +pos_normalize: True + +# MODEL +projection_dim: 22 # @orion_step1: --projection_dim~"uniform(4, 44, discrete=True)" +spatial_focus_tau: 0.3333 # @orion_step1: --spatial_focus_tau~"uniform(0.01, 1.0, precision=4)" + +input_shape: [null, !ref , !ref , null] +cnn_temporal_kernels: 61 # @orion_step1: --cnn_temporal_kernels~"uniform(32, 72,discrete=True)" +cnn_temporal_kernelsize: 51 # @orion_step1: --cnn_temporal_kernelsize~"uniform(24, 62,discrete=True)" +# depth multiplier for the spatial depthwise conv. layer +cnn_spatial_depth_multiplier: 4 # @orion_step1: --cnn_spatial_depth_multiplier~"uniform(1, 4,discrete=True)" +cnn_spatial_max_norm: 1. # kernel max-norm constaint of the spatial depthwise conv. layer +cnn_spatial_pool: 4 +cnn_septemporal_depth_multiplier: 1 # depth multiplier for the separable temporal conv. layer +cnn_septemporal_point_kernels_ratio_: 7 # @orion_step1: --cnn_septemporal_point_kernels_ratio_~"uniform(0, 8, discrete=True)" +cnn_septemporal_point_kernels_ratio: !ref / 4 +## number of temporal filters in the separable temporal conv. layer +cnn_septemporal_point_kernels_: !ref * * +cnn_septemporal_point_kernels: !apply:math.ceil + - !ref * + 1 +cnn_septemporal_kernelsize_: 15 # @orion_step1: --cnn_septemporal_kernelsize_~"uniform(3, 24,discrete=True)" +max_cnn_spatial_pool: 4 +cnn_septemporal_kernelsize: !apply:round + - !ref * / +cnn_septemporal_pool: 7 # @orion_step1: --cnn_septemporal_pool~"uniform(1, 8,discrete=True)" +cnn_pool_type: "avg" +dense_max_norm: 0.25 # kernel max-norm constaint of the dense layer +dropout: 0.008464 # @orion_step1: --dropout~"uniform(0.0, 0.5)" +activation_type: "elu" + +model: !new:models.SpatialEEGNet.SpatialEEGNet + input_shape: !ref + cnn_temporal_kernels: !ref + cnn_temporal_kernelsize: [!ref , 1] + cnn_spatial_depth_multiplier: !ref + cnn_spatial_max_norm: !ref + cnn_spatial_pool: [!ref , 1] + cnn_septemporal_depth_multiplier: !ref + cnn_septemporal_point_kernels: !ref + cnn_septemporal_kernelsize: [!ref , 1] + cnn_septemporal_pool: [!ref , 1] + cnn_pool_type: !ref + activation_type: !ref + spatial_focus: !new:models.SpatialEEGNet.SpatialFocus + projection_dim: !ref + position_dim: 3 + tau: !ref + dense_max_norm: !ref + dropout: !ref + dense_n_neurons: !ref diff --git a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet_cross.yaml b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet_cross.yaml new file mode 100644 index 000000000..00f7c5874 --- /dev/null +++ b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet_cross.yaml @@ -0,0 +1,187 @@ +seed: 1234 +__set_torchseed: !apply:torch.manual_seed [!ref ] + +# DIRECTORIES +data_folder: + !PLACEHOLDER #'/path/to/dataset'. The dataset will be automatically downloaded in this folder + + +cached_data_folder: !PLACEHOLDER #'path/to/pickled/dataset' + + +output_folder: !PLACEHOLDER + #'path/to/results' + + # DATASET HPARS + # Defining the MOABB dataset. + + +datasets: + - !new:moabb.datasets.BNCI2014001 +save_prepared_dataset: True # set to True if you want to save the prepared dataset as a pkl file to load and use afterwards +data_iterator_name: !PLACEHOLDER +target_subject_idx: !PLACEHOLDER +target_session_idx: !PLACEHOLDER +events_to_load: null # all events will be loaded +original_sample_rate: 250 # Original sampling rate provided by dataset authors +sample_rate: 125 # Target sampling rate (Hz) +# band-pass filtering cut-off frequencies +fmin: 0.13 # orion_step1: --fmin~"uniform(0.1, 5, precision=2)" +fmax: 46.0 # @orion_step1: --fmax~"uniform(20.0, 50.0, precision=3)" +n_classes: 4 +# tmin, tmax respect to stimulus onset that define the interval attribute of the dataset class +# trial begins (0 s), cue (2 s, 1.25 s long); each trial is 6 s long +# dataset interval starts from 2 +# -->tmin tmax are referred to this start value (e.g., tmin=0.5 corresponds to 2.5 s) +tmin: 0. +tmax: 4.0 # @orion_step1: --tmax~"uniform(1.0, 4.0, precision=2)" +n_steps_channel_selection: null +T: !apply:math.ceil + - !ref * ( - ) +C: 22 +# We here specify how to perfom test: +# - If test_with: 'last' we perform test with the latest model. +# - if test_with: 'best, we perform test with the best model (according to the metric specified in test_key) +# The variable avg_models can be used to average the parameters of the last (or best) N saved models before testing. +# This can have a regularization effect. If avg_models: 1, the last (or best) model is used directly. +test_with: "last" # 'last' or 'best' +test_key: "acc" # Possible opts: "loss", "f1", "auc", "acc" + +# METRICS +f1: !name:sklearn.metrics.f1_score + average: "macro" +acc: !name:sklearn.metrics.balanced_accuracy_score +cm: !name:sklearn.metrics.confusion_matrix +metrics: + f1: !ref + acc: !ref + cm: !ref +# TRAINING HPARS +n_train_examples: 100 # it will be replaced in the train script +# checkpoints to average +avg_models: 10 # @orion_step1: --avg_models~"uniform(1, 15,discrete=True)" +number_of_epochs: 100 # orion_step1: --number_of_epochs~"uniform(500, 1000, discrete=True)" +lr: 0.0001 # orion_step1: --lr~"choices([0.01, 0.005, 0.001, 0.0005, 0.0001])" +# Learning rate scheduling (cyclic learning rate is used here) +max_lr: !ref # Upper bound of the cycle (max value of the lr) +base_lr: 0.00000001 # Lower bound in the cycle (min value of the lr) +step_size_multiplier: 5 #from 2 to 8 +step_size: !apply:round + - !ref * / +lr_annealing: !new:speechbrain.nnet.schedulers.CyclicLRScheduler + base_lr: !ref + max_lr: !ref + step_size: !ref +label_smoothing: 0.0 +loss: !name:speechbrain.nnet.losses.nll_loss + label_smoothing: !ref +optimizer: !name:torch.optim.Adam + lr: !ref +epoch_counter: !new:speechbrain.utils.epoch_loop.EpochCounter # epoch counter + limit: !ref +batch_size_exponent: 4 # @orion_step1: --batch_size_exponent~"uniform(4, 6,discrete=True)" +batch_size: !ref 2 ** +valid_ratio: 0.2 + +# DATA AUGMENTATION +# cutcat (disabled when min_num_segments=max_num_segments=1) +max_num_segments: 3 # @orion_step2: --max_num_segments~"uniform(2, 6, discrete=True)" +cutcat: !new:speechbrain.augment.time_domain.CutCat + min_num_segments: 2 + max_num_segments: !ref +# random amplitude gain between 0.5-1.5 uV (disabled when amp_delta=0.) +amp_delta: 0.01742 # @orion_step2: --amp_delta~"uniform(0.0, 0.5)" +rand_amp: !new:speechbrain.augment.time_domain.RandAmp + amp_low: !ref 1 - + amp_high: !ref 1 + +# random shifts between -300 ms to 300 ms (disabled when shift_delta=0.) +shift_delta_: 1 # @orion_step2: --shift_delta_~"uniform(0, 25, discrete=True)" +shift_delta: !ref 1e-2 * # 0.250 # 0.-0.25 with steps of 0.01 +min_shift: !apply:math.floor + - !ref 0 - * +max_shift: !apply:math.floor + - !ref 0 + * +time_shift: !new:speechbrain.augment.freq_domain.RandomShift + min_shift: !ref + max_shift: !ref + dim: 1 +# injection of gaussian white noise +snr_white_low: 15.0 # @orion_step2: --snr_white_low~"uniform(0.0, 15, precision=2)" +snr_white_delta: 19.1 # @orion_step2: --snr_white_delta~"uniform(5.0, 20.0, precision=3)" +snr_white_high: !ref + +add_noise_white: !new:speechbrain.augment.time_domain.AddNoise + snr_low: !ref + snr_high: !ref +position_noise_sigma: 0.01 # @orion_step1: --position_noise_sigma~"uniform(0.0, 0.1, precision=4)" + +repeat_augment: 1 # @orion_step1: --repeat_augment 0 +graph_augment: !new:torch.nn.Sequential + - !new:utils.graph_iterators.PositionNoise + sigma: !ref + # - !new:utils.graph_iterators.NodeDrop + # choose_k: 17 +augment: !new:speechbrain.augment.augmenter.Augmenter + parallel_augment: True + concat_original: True + parallel_augment_fixed_bs: True + repeat_augment: !ref + shuffle_augmentations: True + min_augmentations: 4 + max_augmentations: 4 + augmentations: + [!ref , !ref , !ref , !ref ] + +# DATA NORMALIZATION +dims_to_normalize: 1 # 1 (time) or 2 (EEG channels) +normalize: !name:speechbrain.processing.signal_processing.mean_std_norm + dims: !ref +pos_normalize: True + +# MODEL +projection_dim: 22 # @orion_step1: --projection_dim~"uniform(4, 44, discrete=True)" +spatial_focus_tau: 0.3333 # @orion_step1: --spatial_focus_tau~"uniform(0.001, 1.0, precision=4)" + +input_shape: [null, !ref , !ref , null] +cnn_temporal_kernels: 61 # @orion_step1: --cnn_temporal_kernels~"uniform(32, 72,discrete=True)" +cnn_temporal_kernelsize: 51 # @orion_step1: --cnn_temporal_kernelsize~"uniform(24, 62,discrete=True)" +# depth multiplier for the spatial depthwise conv. layer +cnn_spatial_depth_multiplier: 4 # @orion_step1: --cnn_spatial_depth_multiplier~"uniform(1, 4,discrete=True)" +cnn_spatial_max_norm: 1. # kernel max-norm constaint of the spatial depthwise conv. layer +cnn_spatial_pool: 4 +cnn_septemporal_depth_multiplier: 1 # depth multiplier for the separable temporal conv. layer +cnn_septemporal_point_kernels_ratio_: 7 # @orion_step1: --cnn_septemporal_point_kernels_ratio_~"uniform(0, 8, discrete=True)" +cnn_septemporal_point_kernels_ratio: !ref / 4 +## number of temporal filters in the separable temporal conv. layer +cnn_septemporal_point_kernels_: !ref * * +cnn_septemporal_point_kernels: !apply:math.ceil + - !ref * + 1 +cnn_septemporal_kernelsize_: 15 # @orion_step1: --cnn_septemporal_kernelsize_~"uniform(3, 24,discrete=True)" +max_cnn_spatial_pool: 4 +cnn_septemporal_kernelsize: !apply:round + - !ref * / +cnn_septemporal_pool: 7 # @orion_step1: --cnn_septemporal_pool~"uniform(1, 8,discrete=True)" +cnn_pool_type: "avg" +dense_max_norm: 0.25 # kernel max-norm constaint of the dense layer +dropout: 0.008464 # @orion_step1: --dropout~"uniform(0.0, 0.5)" +activation_type: "elu" + +model: !new:models.SpatialEEGNet.SpatialEEGNet + input_shape: !ref + cnn_temporal_kernels: !ref + cnn_temporal_kernelsize: [!ref , 1] + cnn_spatial_depth_multiplier: !ref + cnn_spatial_max_norm: !ref + cnn_spatial_pool: [!ref , 1] + cnn_septemporal_depth_multiplier: !ref + cnn_septemporal_point_kernels: !ref + cnn_septemporal_kernelsize: [!ref , 1] + cnn_septemporal_pool: [!ref , 1] + cnn_pool_type: !ref + activation_type: !ref + spatial_focus: !new:models.SpatialEEGNet.SpatialFocus + projection_dim: !ref + position_dim: 3 + tau: !ref + dense_max_norm: !ref + dropout: !ref + dense_n_neurons: !ref diff --git a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014004/SpatialEEGNet_cross.yaml b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014004/SpatialEEGNet_cross.yaml new file mode 100644 index 000000000..c8c465a80 --- /dev/null +++ b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014004/SpatialEEGNet_cross.yaml @@ -0,0 +1,187 @@ +seed: 1234 +__set_torchseed: !apply:torch.manual_seed [!ref ] + +# DIRECTORIES +data_folder: + !PLACEHOLDER #'/path/to/dataset'. The dataset will be automatically downloaded in this folder + + +cached_data_folder: !PLACEHOLDER #'path/to/pickled/dataset' + + +output_folder: !PLACEHOLDER + #'path/to/results' + + # DATASET HPARS + # Defining the MOABB dataset. + + +datasets: + - !new:moabb.datasets.BNCI2014004 +save_prepared_dataset: True # set to True if you want to save the prepared dataset as a pkl file to load and use afterwards +data_iterator_name: !PLACEHOLDER +target_subject_idx: !PLACEHOLDER +target_session_idx: !PLACEHOLDER +events_to_load: null # all events will be loaded +original_sample_rate: 250 # Original sampling rate provided by dataset authors +sample_rate: 125 # Target sampling rate (Hz) +# band-pass filtering cut-off frequencies +fmin: 0.97 # orion_step1: --fmin~"uniform(0.1, 5, precision=2)" +fmax: 46.0 # @orion_step1: --fmax~"uniform(20.0, 50.0, precision=3)" +n_classes: 2 +# tmin, tmax respect to stimulus onset that define the interval attribute of the dataset class +# trial begins (0 s), cue (2 s, 1.25 s long); each trial is 6 s long +# dataset interval starts from 2 +# -->tmin tmax are referred to this start value (e.g., tmin=0.5 corresponds to 2.5 s) +tmin: 0. +tmax: 4.0 # @orion_step1: --tmax~"uniform(1.0, 4.0, precision=2)" +n_steps_channel_selection: null +T: !apply:math.ceil + - !ref * ( - ) +C: 3 +# We here specify how to perfom test: +# - If test_with: 'last' we perform test with the latest model. +# - if test_with: 'best, we perform test with the best model (according to the metric specified in test_key) +# The variable avg_models can be used to average the parameters of the last (or best) N saved models before testing. +# This can have a regularization effect. If avg_models: 1, the last (or best) model is used directly. +test_with: "last" # 'last' or 'best' +test_key: "acc" # Possible opts: "loss", "f1", "auc", "acc" + +# METRICS +f1: !name:sklearn.metrics.f1_score + average: "macro" +acc: !name:sklearn.metrics.balanced_accuracy_score +cm: !name:sklearn.metrics.confusion_matrix +metrics: + f1: !ref + acc: !ref + cm: !ref +# TRAINING HPARS +n_train_examples: 100 # it will be replaced in the train script +# checkpoints to average +avg_models: 10 # @orion_step1: --avg_models~"uniform(1, 15,discrete=True)" +number_of_epochs: 100 # orion_step1: --number_of_epochs~"uniform(500, 1000, discrete=True)" +lr: 0.0001 # orion_step1: --lr~"choices([0.01, 0.005, 0.001, 0.0005, 0.0001])" +# Learning rate scheduling (cyclic learning rate is used here) +max_lr: !ref # Upper bound of the cycle (max value of the lr) +base_lr: 0.00000001 # Lower bound in the cycle (min value of the lr) +step_size_multiplier: 5 #from 2 to 8 +step_size: !apply:round + - !ref * / +lr_annealing: !new:speechbrain.nnet.schedulers.CyclicLRScheduler + base_lr: !ref + max_lr: !ref + step_size: !ref +label_smoothing: 0.0 +loss: !name:speechbrain.nnet.losses.nll_loss + label_smoothing: !ref +optimizer: !name:torch.optim.Adam + lr: !ref +epoch_counter: !new:speechbrain.utils.epoch_loop.EpochCounter # epoch counter + limit: !ref +batch_size_exponent: 4 # @orion_step1: --batch_size_exponent~"uniform(4, 6,discrete=True)" +batch_size: !ref 2 ** +valid_ratio: 0.2 + +# DATA AUGMENTATION +# cutcat (disabled when min_num_segments=max_num_segments=1) +max_num_segments: 3 # @orion_step2: --max_num_segments~"uniform(2, 6, discrete=True)" +cutcat: !new:speechbrain.augment.time_domain.CutCat + min_num_segments: 2 + max_num_segments: !ref +# random amplitude gain between 0.5-1.5 uV (disabled when amp_delta=0.) +amp_delta: 0.01742 # @orion_step2: --amp_delta~"uniform(0.0, 0.5)" +rand_amp: !new:speechbrain.augment.time_domain.RandAmp + amp_low: !ref 1 - + amp_high: !ref 1 + +# random shifts between -300 ms to 300 ms (disabled when shift_delta=0.) +shift_delta_: 1 # @orion_step2: --shift_delta_~"uniform(0, 25, discrete=True)" +shift_delta: !ref 1e-2 * # 0.250 # 0.-0.25 with steps of 0.01 +min_shift: !apply:math.floor + - !ref 0 - * +max_shift: !apply:math.floor + - !ref 0 + * +time_shift: !new:speechbrain.augment.freq_domain.RandomShift + min_shift: !ref + max_shift: !ref + dim: 1 +# injection of gaussian white noise +snr_white_low: 15.0 # @orion_step2: --snr_white_low~"uniform(0.0, 15, precision=2)" +snr_white_delta: 19.1 # @orion_step2: --snr_white_delta~"uniform(5.0, 20.0, precision=3)" +snr_white_high: !ref + +add_noise_white: !new:speechbrain.augment.time_domain.AddNoise + snr_low: !ref + snr_high: !ref +position_noise_sigma: 0.01 # @orion_step1: --position_noise_sigma~"uniform(0.0, 0.1, precision=4)" + +repeat_augment: 1 # @orion_step1: --repeat_augment 0 +graph_augment: !new:torch.nn.Sequential + - !new:utils.graph_iterators.PositionNoise + sigma: !ref + # - !new:utils.graph_iterators.NodeDrop + # choose_k: 17 +augment: !new:speechbrain.augment.augmenter.Augmenter + parallel_augment: True + concat_original: True + parallel_augment_fixed_bs: True + repeat_augment: !ref + shuffle_augmentations: True + min_augmentations: 4 + max_augmentations: 4 + augmentations: + [!ref , !ref , !ref , !ref ] + +# DATA NORMALIZATION +dims_to_normalize: 1 # 1 (time) or 2 (EEG channels) +normalize: !name:speechbrain.processing.signal_processing.mean_std_norm + dims: !ref +pos_normalize: True + +# MODEL +projection_dim: 22 # @orion_step1: --projection_dim~"uniform(4, 44, discrete=True)" +spatial_focus_tau: 0.3333 # @orion_step1: --spatial_focus_tau~"uniform(0.001, 1.0, precision=4)" + +input_shape: [null, !ref , !ref , null] +cnn_temporal_kernels: 61 # @orion_step1: --cnn_temporal_kernels~"uniform(32, 72,discrete=True)" +cnn_temporal_kernelsize: 51 # @orion_step1: --cnn_temporal_kernelsize~"uniform(24, 62,discrete=True)" +# depth multiplier for the spatial depthwise conv. layer +cnn_spatial_depth_multiplier: 4 # @orion_step1: --cnn_spatial_depth_multiplier~"uniform(1, 4,discrete=True)" +cnn_spatial_max_norm: 1. # kernel max-norm constaint of the spatial depthwise conv. layer +cnn_spatial_pool: 4 +cnn_septemporal_depth_multiplier: 1 # depth multiplier for the separable temporal conv. layer +cnn_septemporal_point_kernels_ratio_: 7 # @orion_step1: --cnn_septemporal_point_kernels_ratio_~"uniform(0, 8, discrete=True)" +cnn_septemporal_point_kernels_ratio: !ref / 4 +## number of temporal filters in the separable temporal conv. layer +cnn_septemporal_point_kernels_: !ref * * +cnn_septemporal_point_kernels: !apply:math.ceil + - !ref * + 1 +cnn_septemporal_kernelsize_: 15 # @orion_step1: --cnn_septemporal_kernelsize_~"uniform(3, 24,discrete=True)" +max_cnn_spatial_pool: 4 +cnn_septemporal_kernelsize: !apply:round + - !ref * / +cnn_septemporal_pool: 7 # @orion_step1: --cnn_septemporal_pool~"uniform(1, 8,discrete=True)" +cnn_pool_type: "avg" +dense_max_norm: 0.25 # kernel max-norm constaint of the dense layer +dropout: 0.008464 # @orion_step1: --dropout~"uniform(0.0, 0.5)" +activation_type: "elu" + +model: !new:models.SpatialEEGNet.SpatialEEGNet + input_shape: !ref + cnn_temporal_kernels: !ref + cnn_temporal_kernelsize: [!ref , 1] + cnn_spatial_depth_multiplier: !ref + cnn_spatial_max_norm: !ref + cnn_spatial_pool: [!ref , 1] + cnn_septemporal_depth_multiplier: !ref + cnn_septemporal_point_kernels: !ref + cnn_septemporal_kernelsize: [!ref , 1] + cnn_septemporal_pool: [!ref , 1] + cnn_pool_type: !ref + activation_type: !ref + spatial_focus: !new:models.SpatialEEGNet.SpatialFocus + projection_dim: !ref + position_dim: 3 + tau: !ref + dense_max_norm: !ref + dropout: !ref + dense_n_neurons: !ref diff --git a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014004/SpatialEEGNet_single.yaml b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014004/SpatialEEGNet_single.yaml new file mode 100644 index 000000000..df4516fba --- /dev/null +++ b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014004/SpatialEEGNet_single.yaml @@ -0,0 +1,187 @@ +seed: 1234 +__set_torchseed: !apply:torch.manual_seed [!ref ] + +# DIRECTORIES +data_folder: + !PLACEHOLDER #'/path/to/dataset'. The dataset will be automatically downloaded in this folder + + +cached_data_folder: !PLACEHOLDER #'path/to/pickled/dataset' + + +output_folder: !PLACEHOLDER + #'path/to/results' + + # DATASET HPARS + # Defining the MOABB dataset. + + +datasets: + - !new:moabb.datasets.BNCI2014004 +save_prepared_dataset: True # set to True if you want to save the prepared dataset as a pkl file to load and use afterwards +data_iterator_name: !PLACEHOLDER +target_subject_idx: !PLACEHOLDER +target_session_idx: !PLACEHOLDER +events_to_load: null # all events will be loaded +original_sample_rate: 250 # Original sampling rate provided by dataset authors +sample_rate: 125 # Target sampling rate (Hz) +# band-pass filtering cut-off frequencies +fmin: 0.13 # orion_step1: --fmin~"uniform(0.1, 5, precision=2)" +fmax: 46.0 # @orion_step1: --fmax~"uniform(20.0, 50.0, precision=3)" +n_classes: 4 +# tmin, tmax respect to stimulus onset that define the interval attribute of the dataset class +# trial begins (0 s), cue (2 s, 1.25 s long); each trial is 6 s long +# dataset interval starts from 2 +# -->tmin tmax are referred to this start value (e.g., tmin=0.5 corresponds to 2.5 s) +tmin: 0. +tmax: 4.0 # @orion_step1: --tmax~"uniform(1.0, 4.0, precision=2)" +n_steps_channel_selection: null +T: !apply:math.ceil + - !ref * ( - ) +C: 3 +# We here specify how to perfom test: +# - If test_with: 'last' we perform test with the latest model. +# - if test_with: 'best, we perform test with the best model (according to the metric specified in test_key) +# The variable avg_models can be used to average the parameters of the last (or best) N saved models before testing. +# This can have a regularization effect. If avg_models: 1, the last (or best) model is used directly. +test_with: "last" # 'last' or 'best' +test_key: "acc" # Possible opts: "loss", "f1", "auc", "acc" + +# METRICS +f1: !name:sklearn.metrics.f1_score + average: "macro" +acc: !name:sklearn.metrics.balanced_accuracy_score +cm: !name:sklearn.metrics.confusion_matrix +metrics: + f1: !ref + acc: !ref + # cm: !ref +# TRAINING HPARS +n_train_examples: 100 # it will be replaced in the train script +# checkpoints to average +avg_models: 10 # @orion_step1: --avg_models~"uniform(1, 15,discrete=True)" +number_of_epochs: 100 # @orion_step1: --number_of_epochs~"uniform(250, 1000, discrete=True)" +lr: 0.0001 # orion_step1: --lr~"choices([0.01, 0.005, 0.001, 0.0005, 0.0001])" +# Learning rate scheduling (cyclic learning rate is used here) +max_lr: !ref # Upper bound of the cycle (max value of the lr) +base_lr: 0.00000001 # Lower bound in the cycle (min value of the lr) +step_size_multiplier: 5 #from 2 to 8 +step_size: !apply:round + - !ref * / +lr_annealing: !new:speechbrain.nnet.schedulers.CyclicLRScheduler + base_lr: !ref + max_lr: !ref + step_size: !ref +label_smoothing: 0.0 +loss: !name:speechbrain.nnet.losses.nll_loss + label_smoothing: !ref +optimizer: !name:torch.optim.Adam + lr: !ref +epoch_counter: !new:speechbrain.utils.epoch_loop.EpochCounter # epoch counter + limit: !ref +batch_size_exponent: 4 # @orion_step1: --batch_size_exponent~"uniform(4, 6,discrete=True)" +batch_size: !ref 2 ** +valid_ratio: 0.2 + +# DATA AUGMENTATION +# cutcat (disabled when min_num_segments=max_num_segments=1) +max_num_segments: 3 # @orion_step2: --max_num_segments~"uniform(2, 6, discrete=True)" +cutcat: !new:speechbrain.augment.time_domain.CutCat + min_num_segments: 2 + max_num_segments: !ref +# random amplitude gain between 0.5-1.5 uV (disabled when amp_delta=0.) +amp_delta: 0.01742 # @orion_step2: --amp_delta~"uniform(0.0, 0.5)" +rand_amp: !new:speechbrain.augment.time_domain.RandAmp + amp_low: !ref 1 - + amp_high: !ref 1 + +# random shifts between -300 ms to 300 ms (disabled when shift_delta=0.) +shift_delta_: 1 # @orion_step2: --shift_delta_~"uniform(0, 25, discrete=True)" +shift_delta: !ref 1e-2 * # 0.250 # 0.-0.25 with steps of 0.01 +min_shift: !apply:math.floor + - !ref 0 - * +max_shift: !apply:math.floor + - !ref 0 + * +time_shift: !new:speechbrain.augment.freq_domain.RandomShift + min_shift: !ref + max_shift: !ref + dim: 1 +# injection of gaussian white noise +snr_white_low: 15.0 # @orion_step2: --snr_white_low~"uniform(0.0, 15, precision=2)" +snr_white_delta: 19.1 # @orion_step2: --snr_white_delta~"uniform(5.0, 20.0, precision=3)" +snr_white_high: !ref + +add_noise_white: !new:speechbrain.augment.time_domain.AddNoise + snr_low: !ref + snr_high: !ref +position_noise_sigma: 0.01 # @orion_step1: --position_noise_sigma~"uniform(0.0, 0.1, precision=4)" + +repeat_augment: 1 # @orion_step1: --repeat_augment 0 +graph_augment: !new:torch.nn.Sequential + - !new:utils.graph_iterators.PositionNoise + sigma: !ref + # - !new:utils.graph_iterators.NodeDrop + # choose_k: 17 +augment: !new:speechbrain.augment.augmenter.Augmenter + parallel_augment: True + concat_original: True + parallel_augment_fixed_bs: True + repeat_augment: !ref + shuffle_augmentations: True + min_augmentations: 4 + max_augmentations: 4 + augmentations: + [!ref , !ref , !ref , !ref ] + +# DATA NORMALIZATION +dims_to_normalize: 1 # 1 (time) or 2 (EEG channels) +normalize: !name:speechbrain.processing.signal_processing.mean_std_norm + dims: !ref +pos_normalize: True + +# MODEL +projection_dim: 22 # @orion_step1: --projection_dim~"uniform(4, 44, discrete=True)" +spatial_focus_tau: 0.3333 # @orion_step1: --spatial_focus_tau~"uniform(0.001, 1.0, precision=4)" + +input_shape: [null, !ref , !ref , null] +cnn_temporal_kernels: 61 # @orion_step1: --cnn_temporal_kernels~"uniform(32, 72,discrete=True)" +cnn_temporal_kernelsize: 51 # @orion_step1: --cnn_temporal_kernelsize~"uniform(24, 62,discrete=True)" +# depth multiplier for the spatial depthwise conv. layer +cnn_spatial_depth_multiplier: 4 # @orion_step1: --cnn_spatial_depth_multiplier~"uniform(1, 4,discrete=True)" +cnn_spatial_max_norm: 1. # kernel max-norm constaint of the spatial depthwise conv. layer +cnn_spatial_pool: 4 +cnn_septemporal_depth_multiplier: 1 # depth multiplier for the separable temporal conv. layer +cnn_septemporal_point_kernels_ratio_: 7 # @orion_step1: --cnn_septemporal_point_kernels_ratio_~"uniform(0, 8, discrete=True)" +cnn_septemporal_point_kernels_ratio: !ref / 4 +## number of temporal filters in the separable temporal conv. layer +cnn_septemporal_point_kernels_: !ref * * +cnn_septemporal_point_kernels: !apply:math.ceil + - !ref * + 1 +cnn_septemporal_kernelsize_: 15 # @orion_step1: --cnn_septemporal_kernelsize_~"uniform(3, 24,discrete=True)" +max_cnn_spatial_pool: 4 +cnn_septemporal_kernelsize: !apply:round + - !ref * / +cnn_septemporal_pool: 7 # @orion_step1: --cnn_septemporal_pool~"uniform(1, 8,discrete=True)" +cnn_pool_type: "avg" +dense_max_norm: 0.25 # kernel max-norm constaint of the dense layer +dropout: 0.008464 # @orion_step1: --dropout~"uniform(0.0, 0.5)" +activation_type: "elu" + +model: !new:models.SpatialEEGNet.SpatialEEGNet + input_shape: !ref + cnn_temporal_kernels: !ref + cnn_temporal_kernelsize: [!ref , 1] + cnn_spatial_depth_multiplier: !ref + cnn_spatial_max_norm: !ref + cnn_spatial_pool: [!ref , 1] + cnn_septemporal_depth_multiplier: !ref + cnn_septemporal_point_kernels: !ref + cnn_septemporal_kernelsize: [!ref , 1] + cnn_septemporal_pool: [!ref , 1] + cnn_pool_type: !ref + activation_type: !ref + spatial_focus: !new:models.SpatialEEGNet.SpatialFocus + projection_dim: !ref + position_dim: 3 + tau: !ref + dense_max_norm: !ref + dropout: !ref + dense_n_neurons: !ref diff --git a/benchmarks/MOABB/hparams/MotorImagery/BNCI2015001/SpatialEEGNet_cross.yaml b/benchmarks/MOABB/hparams/MotorImagery/BNCI2015001/SpatialEEGNet_cross.yaml new file mode 100644 index 000000000..5dc44ada2 --- /dev/null +++ b/benchmarks/MOABB/hparams/MotorImagery/BNCI2015001/SpatialEEGNet_cross.yaml @@ -0,0 +1,187 @@ +seed: 1234 +__set_torchseed: !apply:torch.manual_seed [!ref ] + +# DIRECTORIES +data_folder: + !PLACEHOLDER #'/path/to/dataset'. The dataset will be automatically downloaded in this folder + + +cached_data_folder: !PLACEHOLDER #'path/to/pickled/dataset' + + +output_folder: !PLACEHOLDER + #'path/to/results' + + # DATASET HPARS + # Defining the MOABB dataset. + + +datasets: + - !new:moabb.datasets.BNCI2015001 +save_prepared_dataset: True # set to True if you want to save the prepared dataset as a pkl file to load and use afterwards +data_iterator_name: !PLACEHOLDER +target_subject_idx: !PLACEHOLDER +target_session_idx: !PLACEHOLDER +events_to_load: null # all events will be loaded +original_sample_rate: 512 # Original sampling rate provided by dataset authors +sample_rate: 128 # Target sampling rate (Hz) +# band-pass filtering cut-off frequencies +fmin: 3.4 # orion_step1: --fmin~"uniform(0.1, 5, precision=2)" +fmax: 46.0 # @orion_step1: --fmax~"uniform(20.0, 50.0, precision=3)" +n_classes: 2 +# tmin, tmax respect to stimulus onset that define the interval attribute of the dataset class +# trial begins (0 s), cue (2 s, 1.25 s long); each trial is 6 s long +# dataset interval starts from 2 +# -->tmin tmax are referred to this start value (e.g., tmin=0.5 corresponds to 2.5 s) +tmin: 0. +tmax: 4.0 # @orion_step1: --tmax~"uniform(1.0, 4.0, precision=2)" +n_steps_channel_selection: null +T: !apply:math.ceil + - !ref * ( - ) +C: 13 +# We here specify how to perfom test: +# - If test_with: 'last' we perform test with the latest model. +# - if test_with: 'best, we perform test with the best model (according to the metric specified in test_key) +# The variable avg_models can be used to average the parameters of the last (or best) N saved models before testing. +# This can have a regularization effect. If avg_models: 1, the last (or best) model is used directly. +test_with: "last" # 'last' or 'best' +test_key: "acc" # Possible opts: "loss", "f1", "auc", "acc" + +# METRICS +f1: !name:sklearn.metrics.f1_score + average: "macro" +acc: !name:sklearn.metrics.balanced_accuracy_score +cm: !name:sklearn.metrics.confusion_matrix +metrics: + f1: !ref + acc: !ref + cm: !ref +# TRAINING HPARS +n_train_examples: 100 # it will be replaced in the train script +# checkpoints to average +avg_models: 10 # @orion_step1: --avg_models~"uniform(1, 15,discrete=True)" +number_of_epochs: 100 # orion_step1: --number_of_epochs~"uniform(500, 1000, discrete=True)" +lr: 0.0001 # orion_step1: --lr~"choices([0.01, 0.005, 0.001, 0.0005, 0.0001])" +# Learning rate scheduling (cyclic learning rate is used here) +max_lr: !ref # Upper bound of the cycle (max value of the lr) +base_lr: 0.00000001 # Lower bound in the cycle (min value of the lr) +step_size_multiplier: 5 #from 2 to 8 +step_size: !apply:round + - !ref * / +lr_annealing: !new:speechbrain.nnet.schedulers.CyclicLRScheduler + base_lr: !ref + max_lr: !ref + step_size: !ref +label_smoothing: 0.0 +loss: !name:speechbrain.nnet.losses.nll_loss + label_smoothing: !ref +optimizer: !name:torch.optim.Adam + lr: !ref +epoch_counter: !new:speechbrain.utils.epoch_loop.EpochCounter # epoch counter + limit: !ref +batch_size_exponent: 4 # @orion_step1: --batch_size_exponent~"uniform(4, 6,discrete=True)" +batch_size: !ref 2 ** +valid_ratio: 0.2 + +# DATA AUGMENTATION +# cutcat (disabled when min_num_segments=max_num_segments=1) +max_num_segments: 3 # @orion_step2: --max_num_segments~"uniform(2, 6, discrete=True)" +cutcat: !new:speechbrain.augment.time_domain.CutCat + min_num_segments: 2 + max_num_segments: !ref +# random amplitude gain between 0.5-1.5 uV (disabled when amp_delta=0.) +amp_delta: 0.01742 # @orion_step2: --amp_delta~"uniform(0.0, 0.5)" +rand_amp: !new:speechbrain.augment.time_domain.RandAmp + amp_low: !ref 1 - + amp_high: !ref 1 + +# random shifts between -300 ms to 300 ms (disabled when shift_delta=0.) +shift_delta_: 1 # @orion_step2: --shift_delta_~"uniform(0, 25, discrete=True)" +shift_delta: !ref 1e-2 * # 0.250 # 0.-0.25 with steps of 0.01 +min_shift: !apply:math.floor + - !ref 0 - * +max_shift: !apply:math.floor + - !ref 0 + * +time_shift: !new:speechbrain.augment.freq_domain.RandomShift + min_shift: !ref + max_shift: !ref + dim: 1 +# injection of gaussian white noise +snr_white_low: 15.0 # @orion_step2: --snr_white_low~"uniform(0.0, 15, precision=2)" +snr_white_delta: 19.1 # @orion_step2: --snr_white_delta~"uniform(5.0, 20.0, precision=3)" +snr_white_high: !ref + +add_noise_white: !new:speechbrain.augment.time_domain.AddNoise + snr_low: !ref + snr_high: !ref +position_noise_sigma: 0.01 # @orion_step1: --position_noise_sigma~"uniform(0.0, 0.1, precision=4)" + +repeat_augment: 1 # @orion_step1: --repeat_augment 0 +graph_augment: !new:torch.nn.Sequential + - !new:utils.graph_iterators.PositionNoise + sigma: !ref + # - !new:utils.graph_iterators.NodeDrop + # choose_k: 17 +augment: !new:speechbrain.augment.augmenter.Augmenter + parallel_augment: True + concat_original: True + parallel_augment_fixed_bs: True + repeat_augment: !ref + shuffle_augmentations: True + min_augmentations: 4 + max_augmentations: 4 + augmentations: + [!ref , !ref , !ref , !ref ] + +# DATA NORMALIZATION +dims_to_normalize: 1 # 1 (time) or 2 (EEG channels) +normalize: !name:speechbrain.processing.signal_processing.mean_std_norm + dims: !ref +pos_normalize: True + +# MODEL +projection_dim: 22 # @orion_step1: --projection_dim~"uniform(4, 44, discrete=True)" +spatial_focus_tau: 0.3333 # @orion_step1: --spatial_focus_tau~"uniform(0.001, 1.0, precision=4)" + +input_shape: [null, !ref , !ref , null] +cnn_temporal_kernels: 61 # @orion_step1: --cnn_temporal_kernels~"uniform(32, 72,discrete=True)" +cnn_temporal_kernelsize: 51 # @orion_step1: --cnn_temporal_kernelsize~"uniform(24, 62,discrete=True)" +# depth multiplier for the spatial depthwise conv. layer +cnn_spatial_depth_multiplier: 4 # @orion_step1: --cnn_spatial_depth_multiplier~"uniform(1, 4,discrete=True)" +cnn_spatial_max_norm: 1. # kernel max-norm constaint of the spatial depthwise conv. layer +cnn_spatial_pool: 4 +cnn_septemporal_depth_multiplier: 1 # depth multiplier for the separable temporal conv. layer +cnn_septemporal_point_kernels_ratio_: 7 # @orion_step1: --cnn_septemporal_point_kernels_ratio_~"uniform(0, 8, discrete=True)" +cnn_septemporal_point_kernels_ratio: !ref / 4 +## number of temporal filters in the separable temporal conv. layer +cnn_septemporal_point_kernels_: !ref * * +cnn_septemporal_point_kernels: !apply:math.ceil + - !ref * + 1 +cnn_septemporal_kernelsize_: 15 # @orion_step1: --cnn_septemporal_kernelsize_~"uniform(3, 24,discrete=True)" +max_cnn_spatial_pool: 4 +cnn_septemporal_kernelsize: !apply:round + - !ref * / +cnn_septemporal_pool: 7 # @orion_step1: --cnn_septemporal_pool~"uniform(1, 8,discrete=True)" +cnn_pool_type: "avg" +dense_max_norm: 0.25 # kernel max-norm constaint of the dense layer +dropout: 0.008464 # @orion_step1: --dropout~"uniform(0.0, 0.5)" +activation_type: "elu" + +model: !new:models.SpatialEEGNet.SpatialEEGNet + input_shape: !ref + cnn_temporal_kernels: !ref + cnn_temporal_kernelsize: [!ref , 1] + cnn_spatial_depth_multiplier: !ref + cnn_spatial_max_norm: !ref + cnn_spatial_pool: [!ref , 1] + cnn_septemporal_depth_multiplier: !ref + cnn_septemporal_point_kernels: !ref + cnn_septemporal_kernelsize: [!ref , 1] + cnn_septemporal_pool: [!ref , 1] + cnn_pool_type: !ref + activation_type: !ref + spatial_focus: !new:models.SpatialEEGNet.SpatialFocus + projection_dim: !ref + position_dim: 3 + tau: !ref + dense_max_norm: !ref + dropout: !ref + dense_n_neurons: !ref diff --git a/benchmarks/MOABB/hparams/MotorImagery/BNCI2015001/SpatialEEGNet_single.yaml b/benchmarks/MOABB/hparams/MotorImagery/BNCI2015001/SpatialEEGNet_single.yaml new file mode 100644 index 000000000..04211aff9 --- /dev/null +++ b/benchmarks/MOABB/hparams/MotorImagery/BNCI2015001/SpatialEEGNet_single.yaml @@ -0,0 +1,176 @@ +seed: 1234 +__set_torchseed: !apply:torch.manual_seed [!ref ] + +# DIRECTORIES +data_folder: !PLACEHOLDER #'/path/to/dataset'. The dataset will be automatically downloaded in this folder +cached_data_folder: !PLACEHOLDER #'path/to/pickled/dataset' +output_folder: !PLACEHOLDER #'path/to/results' + +# DATASET HPARS +# Defining the MOABB dataset. +dataset: !new:moabb.datasets.BNCI2015001 +save_prepared_dataset: True # set to True if you want to save the prepared dataset as a pkl file to load and use afterwards +data_iterator_name: !PLACEHOLDER +target_subject_idx: !PLACEHOLDER +target_session_idx: !PLACEHOLDER +events_to_load: null # all events will be loaded +original_sample_rate: 512 # Original sampling rate provided by dataset authors +sample_rate: 128 # Target sampling rate (Hz) +# band-pass filtering cut-off frequencies +fmin: 3.4 # @orion_step1: --fmin~"uniform(0.1, 5, precision=2)" +fmax: 49.6 # @orion_step1: --fmax~"uniform(20.0, 50.0, precision=3)" +n_classes: 2 +# tmin, tmax respect to stimulus onset that define the interval attribute of the dataset class +# trial begins (0 s), cue (2 s, 1.25 s long); each trial is 6 s long +# dataset interval starts from 2 +# -->tmin tmax are referred to this start value (e.g., tmin=0.5 corresponds to 2.5 s) +tmin: 0. +tmax: 4.0 # @orion_step1: --tmax~"uniform(1.0, 4.0, precision=2)" +# number of steps used when selecting adjacent channels from a seed channel (default at Cz) +n_steps_channel_selection: 3 # @orion_step1: --n_steps_channel_selection~"uniform(1, 3,discrete=True)" +T: !apply:math.ceil + - !ref * ( - ) +C: 13 +# We here specify how to perfom test: +# - If test_with: 'last' we perform test with the latest model. +# - if test_with: 'best, we perform test with the best model (according to the metric specified in test_key) +# The variable avg_models can be used to average the parameters of the last (or best) N saved models before testing. +# This can have a regularization effect. If avg_models: 1, the last (or best) model is used directly. +test_with: 'last' # 'last' or 'best' +test_key: "acc" # Possible opts: "loss", "f1", "auc", "acc" + +# METRICS +f1: !name:sklearn.metrics.f1_score + average: 'macro' +acc: !name:sklearn.metrics.balanced_accuracy_score +cm: !name:sklearn.metrics.confusion_matrix +metrics: + f1: !ref + acc: !ref + # cm: !ref +# TRAINING HPARS +n_train_examples: 100 # it will be replaced in the train script +# checkpoints to average +avg_models: 12 # @orion_step1: --avg_models~"uniform(1, 15,discrete=True)" +number_of_epochs: 760 # @orion_step1: --number_of_epochs~"uniform(250, 1000, discrete=True)" +lr: 0.001 # @orion_step1: --lr~"choices([0.01, 0.005, 0.001, 0.0005, 0.0001])" +# Learning rate scheduling (cyclic learning rate is used here) +max_lr: !ref # Upper bound of the cycle (max value of the lr) +base_lr: 0.00000001 # Lower bound in the cycle (min value of the lr) +step_size_multiplier: 5 #from 2 to 8 +step_size: !apply:round + - !ref * / +lr_annealing: !new:speechbrain.nnet.schedulers.CyclicLRScheduler + base_lr: !ref + max_lr: !ref + step_size: !ref +label_smoothing: 0.0 +loss: !name:speechbrain.nnet.losses.nll_loss + label_smoothing: !ref +optimizer: !name:torch.optim.Adam + lr: !ref +epoch_counter: !new:speechbrain.utils.epoch_loop.EpochCounter # epoch counter + limit: !ref +batch_size_exponent: 4 # @orion_step1: --batch_size_exponent~"uniform(4, 6,discrete=True)" +batch_size: !ref 2 ** +valid_ratio: 0.2 + +# DATA AUGMENTATION +# cutcat (disabled when min_num_segments=max_num_segments=1) +max_num_segments: 2 # @orion_step2: --max_num_segments~"uniform(2, 6, discrete=True)" +cutcat: !new:speechbrain.augment.time_domain.CutCat + min_num_segments: 2 + max_num_segments: !ref +# random amplitude gain between 0.5-1.5 uV (disabled when amp_delta=0.) +amp_delta: 0.05772 # @orion_step2: --amp_delta~"uniform(0.0, 0.5)" +rand_amp: !new:speechbrain.augment.time_domain.RandAmp + amp_low: !ref 1 - + amp_high: !ref 1 + +# random shifts between -300 ms to 300 ms (disabled when shift_delta=0.) +shift_delta_: 0 # orion_step2: --shift_delta_~"uniform(0, 25, discrete=True)" +shift_delta: !ref 1e-2 * # 0.250 # 0.-0.25 with steps of 0.01 +min_shift: !apply:math.floor + - !ref 0 - * +max_shift: !apply:math.floor + - !ref 0 + * +time_shift: !new:speechbrain.augment.freq_domain.RandomShift + min_shift: !ref + max_shift: !ref + dim: 1 +# injection of gaussian white noise +snr_white_low: 5.4 # @orion_step2: --snr_white_low~"uniform(0.0, 15, precision=2)" +snr_white_delta: 16.8 # @orion_step2: --snr_white_delta~"uniform(5.0, 20.0, precision=3)" +snr_white_high: !ref + +add_noise_white: !new:speechbrain.augment.time_domain.AddNoise + snr_low: !ref + snr_high: !ref + +repeat_augment: 1 # @orion_step1: --repeat_augment 0 +augment: !new:speechbrain.augment.augmenter.Augmenter + parallel_augment: True + concat_original: True + parallel_augment_fixed_bs: True + repeat_augment: !ref + shuffle_augmentations: True + min_augmentations: 4 + max_augmentations: 4 + augmentations: [ + !ref , + !ref , + !ref , + !ref ] + +# DATA NORMALIZATION +dims_to_normalize: 1 # 1 (time) or 2 (EEG channels) +normalize: !name:speechbrain.processing.signal_processing.mean_std_norm + dims: !ref +pos_normalize: True + +# MODEL +projection_dim: 22 # @orion_step1: --projection_dim~"uniform(4, 44, discrete=True)" +spatial_focus_tau: 0.3333 # @orion_step1: --spatial_focus_tau~"uniform(0.001, 1.0, precision=4)" + +input_shape: [null, !ref , !ref , null] +cnn_temporal_kernels: 61 # @orion_step1: --cnn_temporal_kernels~"uniform(32, 72,discrete=True)" +cnn_temporal_kernelsize: 51 # @orion_step1: --cnn_temporal_kernelsize~"uniform(24, 62,discrete=True)" +# depth multiplier for the spatial depthwise conv. layer +cnn_spatial_depth_multiplier: 4 # @orion_step1: --cnn_spatial_depth_multiplier~"uniform(1, 4,discrete=True)" +cnn_spatial_max_norm: 1. # kernel max-norm constaint of the spatial depthwise conv. layer +cnn_spatial_pool: 4 +cnn_septemporal_depth_multiplier: 1 # depth multiplier for the separable temporal conv. layer +cnn_septemporal_point_kernels_ratio_: 7 # @orion_step1: --cnn_septemporal_point_kernels_ratio_~"uniform(0, 8, discrete=True)" +cnn_septemporal_point_kernels_ratio: !ref / 4 +## number of temporal filters in the separable temporal conv. layer +cnn_septemporal_point_kernels_: !ref * * +cnn_septemporal_point_kernels: !apply:math.ceil + - !ref * + 1 +cnn_septemporal_kernelsize_: 15 # @orion_step1: --cnn_septemporal_kernelsize_~"uniform(3, 24,discrete=True)" +max_cnn_spatial_pool: 4 +cnn_septemporal_kernelsize: !apply:round + - !ref * / +cnn_septemporal_pool: 7 # @orion_step1: --cnn_septemporal_pool~"uniform(1, 8,discrete=True)" +cnn_pool_type: "avg" +dense_max_norm: 0.25 # kernel max-norm constaint of the dense layer +dropout: 0.008464 # @orion_step1: --dropout~"uniform(0.0, 0.5)" +activation_type: "elu" + +model: !new:models.SpatialEEGNet.SpatialEEGNet + input_shape: !ref + cnn_temporal_kernels: !ref + cnn_temporal_kernelsize: [!ref , 1] + cnn_spatial_depth_multiplier: !ref + cnn_spatial_max_norm: !ref + cnn_spatial_pool: [!ref , 1] + cnn_septemporal_depth_multiplier: !ref + cnn_septemporal_point_kernels: !ref + cnn_septemporal_kernelsize: [!ref , 1] + cnn_septemporal_pool: [!ref , 1] + cnn_pool_type: !ref + activation_type: !ref + spatial_focus: !new:models.SpatialEEGNet.SpatialFocus + projection_dim: !ref + position_dim: 3 + tau: !ref + dense_max_norm: !ref + dropout: !ref + dense_n_neurons: !ref diff --git a/benchmarks/MOABB/hparams/MotorImagery/Lee2019_MI/SpatialEEGNet_single.yaml b/benchmarks/MOABB/hparams/MotorImagery/Lee2019_MI/SpatialEEGNet_single.yaml new file mode 100644 index 000000000..3a54a955e --- /dev/null +++ b/benchmarks/MOABB/hparams/MotorImagery/Lee2019_MI/SpatialEEGNet_single.yaml @@ -0,0 +1,176 @@ +seed: 1234 +__set_torchseed: !apply:torch.manual_seed [!ref ] + +# DIRECTORIES +data_folder: !PLACEHOLDER #'/path/to/dataset'. The dataset will be automatically downloaded in this folder +cached_data_folder: !PLACEHOLDER #'path/to/pickled/dataset' +output_folder: !PLACEHOLDER #'path/to/results' + +# DATASET HPARS +# Defining the MOABB dataset. +dataset: !new:moabb.datasets.Lee2019_MI +save_prepared_dataset: True # set to True if you want to save the prepared dataset as a pkl file to load and use afterwards +data_iterator_name: !PLACEHOLDER +target_subject_idx: !PLACEHOLDER +target_session_idx: !PLACEHOLDER +events_to_load: null # all events will be loaded +original_sample_rate: 1000 # Original sampling rate provided by dataset authors +sample_rate: 125 # Target sampling rate (Hz) +# band-pass filtering cut-off frequencies +fmin: 0.23 # @orion_step1: --fmin~"uniform(0.1, 5, precision=2)" +fmax: 21.1 # @orion_step1: --fmax~"uniform(20.0, 50.0, precision=3)" +n_classes: 2 +# tmin, tmax respect to stimulus onset that define the interval attribute of the dataset class +# trial begins (0 s), cue (2 s, 1.25 s long); each trial is 6 s long +# dataset interval starts from 2 +# -->tmin tmax are referred to this start value (e.g., tmin=0.5 corresponds to 2.5 s) +tmin: 0. +tmax: 3.2 # @orion_step1: --tmax~"uniform(1.0, 4.0, precision=2)" +# number of steps used when selecting adjacent channels from a seed channel (default at Cz) +n_steps_channel_selection: 2 # @orion_step1: --n_steps_channel_selection~"uniform(1, 5,discrete=True)" +T: !apply:math.ceil + - !ref * ( - ) +C: 62 +# We here specify how to perfom test: +# - If test_with: 'last' we perform test with the latest model. +# - if test_with: 'best, we perform test with the best model (according to the metric specified in test_key) +# The variable avg_models can be used to average the parameters of the last (or best) N saved models before testing. +# This can have a regularization effect. If avg_models: 1, the last (or best) model is used directly. +test_with: 'last' # 'last' or 'best' +test_key: "acc" # Possible opts: "loss", "f1", "auc", "acc" + +# METRICS +f1: !name:sklearn.metrics.f1_score + average: 'macro' +acc: !name:sklearn.metrics.balanced_accuracy_score +cm: !name:sklearn.metrics.confusion_matrix +metrics: + f1: !ref + acc: !ref + # cm: !ref +# TRAINING HPARS +n_train_examples: 100 # it will be replaced in the train script +# checkpoints to average +avg_models: 15 # @orion_step1: --avg_models~"uniform(1, 15,discrete=True)" +number_of_epochs: 821 # @orion_step1: --number_of_epochs~"uniform(250, 1000, discrete=True)" +lr: 0.001 # @orion_step1: --lr~"choices([0.01, 0.005, 0.001, 0.0005, 0.0001])" +# Learning rate scheduling (cyclic learning rate is used here) +max_lr: !ref # Upper bound of the cycle (max value of the lr) +base_lr: 0.00000001 # Lower bound in the cycle (min value of the lr) +step_size_multiplier: 5 #from 2 to 8 +step_size: !apply:round + - !ref * / +lr_annealing: !new:speechbrain.nnet.schedulers.CyclicLRScheduler + base_lr: !ref + max_lr: !ref + step_size: !ref +label_smoothing: 0.0 +loss: !name:speechbrain.nnet.losses.nll_loss + label_smoothing: !ref +optimizer: !name:torch.optim.Adam + lr: !ref +epoch_counter: !new:speechbrain.utils.epoch_loop.EpochCounter # epoch counter + limit: !ref +batch_size_exponent: 6 # @orion_step1: --batch_size_exponent~"uniform(4, 6,discrete=True)" +batch_size: !ref 2 ** +valid_ratio: 0.2 + +# DATA AUGMENTATION +# cutcat (disabled when min_num_segments=max_num_segments=1) +max_num_segments: 3 # @orion_step2: --max_num_segments~"uniform(2, 6, discrete=True)" +cutcat: !new:speechbrain.augment.time_domain.CutCat + min_num_segments: 2 + max_num_segments: !ref +# random amplitude gain between 0.5-1.5 uV (disabled when amp_delta=0.) +amp_delta: 0.3492 # @orion_step2: --amp_delta~"uniform(0.0, 0.5)" +rand_amp: !new:speechbrain.augment.time_domain.RandAmp + amp_low: !ref 1 - + amp_high: !ref 1 + +# random shifts between -300 ms to 300 ms (disabled when shift_delta=0.) +shift_delta_: 3 # orion_step2: --shift_delta_~"uniform(0, 25, discrete=True)" +shift_delta: !ref 1e-2 * # 0.250 # 0.-0.25 with steps of 0.01 +min_shift: !apply:math.floor + - !ref 0 - * +max_shift: !apply:math.floor + - !ref 0 + * +time_shift: !new:speechbrain.augment.freq_domain.RandomShift + min_shift: !ref + max_shift: !ref + dim: 1 +# injection of gaussian white noise +snr_white_low: 14.0 # @orion_step2: --snr_white_low~"uniform(0.0, 15, precision=2)" +snr_white_delta: 7.02 # @orion_step2: --snr_white_delta~"uniform(5.0, 20.0, precision=3)" +snr_white_high: !ref + +add_noise_white: !new:speechbrain.augment.time_domain.AddNoise + snr_low: !ref + snr_high: !ref + +repeat_augment: 1 # @orion_step1: --repeat_augment 0 +augment: !new:speechbrain.augment.augmenter.Augmenter + parallel_augment: True + concat_original: True + parallel_augment_fixed_bs: True + repeat_augment: !ref + shuffle_augmentations: True + min_augmentations: 4 + max_augmentations: 4 + augmentations: [ + !ref , + !ref , + !ref , + !ref ] + +# DATA NORMALIZATION +dims_to_normalize: 1 # 1 (time) or 2 (EEG channels) +normalize: !name:speechbrain.processing.signal_processing.mean_std_norm + dims: !ref +pos_normalize: True + +# MODEL +projection_dim: 22 # @orion_step1: --projection_dim~"uniform(4, 44, discrete=True)" +spatial_focus_tau: 0.3333 # @orion_step1: --spatial_focus_tau~"uniform(0.001, 1.0, precision=4)" + +input_shape: [null, !ref , !ref , null] +cnn_temporal_kernels: 61 # @orion_step1: --cnn_temporal_kernels~"uniform(32, 72,discrete=True)" +cnn_temporal_kernelsize: 51 # @orion_step1: --cnn_temporal_kernelsize~"uniform(24, 62,discrete=True)" +# depth multiplier for the spatial depthwise conv. layer +cnn_spatial_depth_multiplier: 4 # @orion_step1: --cnn_spatial_depth_multiplier~"uniform(1, 4,discrete=True)" +cnn_spatial_max_norm: 1. # kernel max-norm constaint of the spatial depthwise conv. layer +cnn_spatial_pool: 4 +cnn_septemporal_depth_multiplier: 1 # depth multiplier for the separable temporal conv. layer +cnn_septemporal_point_kernels_ratio_: 7 # @orion_step1: --cnn_septemporal_point_kernels_ratio_~"uniform(0, 8, discrete=True)" +cnn_septemporal_point_kernels_ratio: !ref / 4 +## number of temporal filters in the separable temporal conv. layer +cnn_septemporal_point_kernels_: !ref * * +cnn_septemporal_point_kernels: !apply:math.ceil + - !ref * + 1 +cnn_septemporal_kernelsize_: 15 # @orion_step1: --cnn_septemporal_kernelsize_~"uniform(3, 24,discrete=True)" +max_cnn_spatial_pool: 4 +cnn_septemporal_kernelsize: !apply:round + - !ref * / +cnn_septemporal_pool: 7 # @orion_step1: --cnn_septemporal_pool~"uniform(1, 8,discrete=True)" +cnn_pool_type: "avg" +dense_max_norm: 0.25 # kernel max-norm constaint of the dense layer +dropout: 0.008464 # @orion_step1: --dropout~"uniform(0.0, 0.5)" +activation_type: "elu" + +model: !new:models.SpatialEEGNet.SpatialEEGNet + input_shape: !ref + cnn_temporal_kernels: !ref + cnn_temporal_kernelsize: [!ref , 1] + cnn_spatial_depth_multiplier: !ref + cnn_spatial_max_norm: !ref + cnn_spatial_pool: [!ref , 1] + cnn_septemporal_depth_multiplier: !ref + cnn_septemporal_point_kernels: !ref + cnn_septemporal_kernelsize: [!ref , 1] + cnn_septemporal_pool: [!ref , 1] + cnn_pool_type: !ref + activation_type: !ref + spatial_focus: !new:models.SpatialEEGNet.SpatialFocus + projection_dim: !ref + position_dim: 3 + tau: !ref + dense_max_norm: !ref + dropout: !ref + dense_n_neurons: !ref diff --git a/benchmarks/MOABB/hparams/P300/BNCI2014009/SpatialEEGNet_cross.yaml b/benchmarks/MOABB/hparams/P300/BNCI2014009/SpatialEEGNet_cross.yaml new file mode 100644 index 000000000..8a3843ba5 --- /dev/null +++ b/benchmarks/MOABB/hparams/P300/BNCI2014009/SpatialEEGNet_cross.yaml @@ -0,0 +1,187 @@ +seed: 1234 +__set_torchseed: !apply:torch.manual_seed [!ref ] + +# DIRECTORIES +data_folder: + !PLACEHOLDER #'/path/to/dataset'. The dataset will be automatically downloaded in this folder + + +cached_data_folder: !PLACEHOLDER #'path/to/pickled/dataset' + + +output_folder: !PLACEHOLDER + #'path/to/results' + + # DATASET HPARS + # Defining the MOABB dataset. + + +datasets: + - !new:moabb.datasets.BNCI2014009 +save_prepared_dataset: True # set to True if you want to save the prepared dataset as a pkl file to load and use afterwards +data_iterator_name: !PLACEHOLDER +target_subject_idx: !PLACEHOLDER +target_session_idx: !PLACEHOLDER +events_to_load: null # all events will be loaded +original_sample_rate: 256 # Original sampling rate provided by dataset authors +sample_rate: 128 # Target sampling rate (Hz) +# band-pass filtering cut-off frequencies +fmin: 0.12 # orion_step1: --fmin~"uniform(0.1, 5, precision=2)" +fmax: 46.0 # @orion_step1: --fmax~"uniform(20.0, 50.0, precision=3)" +n_classes: 2 +# tmin, tmax respect to stimulus onset that define the interval attribute of the dataset class +# trial begins (0 s), cue (2 s, 1.25 s long); each trial is 6 s long +# dataset interval starts from 2 +# -->tmin tmax are referred to this start value (e.g., tmin=0.5 corresponds to 2.5 s) +tmin: 0. +tmax: 0.8 +n_steps_channel_selection: null +T: !apply:math.ceil + - !ref * ( - ) +C: 16 +# We here specify how to perfom test: +# - If test_with: 'last' we perform test with the latest model. +# - if test_with: 'best, we perform test with the best model (according to the metric specified in test_key) +# The variable avg_models can be used to average the parameters of the last (or best) N saved models before testing. +# This can have a regularization effect. If avg_models: 1, the last (or best) model is used directly. +test_with: "last" # 'last' or 'best' +test_key: "f1" # Possible opts: "loss", "f1", "auc", "acc" + +# METRICS +f1: !name:sklearn.metrics.f1_score + average: "macro" +acc: !name:sklearn.metrics.balanced_accuracy_score +cm: !name:sklearn.metrics.confusion_matrix +metrics: + f1: !ref + acc: !ref + cm: !ref +# TRAINING HPARS +n_train_examples: 100 # it will be replaced in the train script +# checkpoints to average +avg_models: 10 # @orion_step1: --avg_models~"uniform(1, 15,discrete=True)" +number_of_epochs: 100 # orion_step1: --number_of_epochs~"uniform(500, 1000, discrete=True)" +lr: 0.0001 # orion_step1: --lr~"choices([0.01, 0.005, 0.001, 0.0005, 0.0001])" +# Learning rate scheduling (cyclic learning rate is used here) +max_lr: !ref # Upper bound of the cycle (max value of the lr) +base_lr: 0.00000001 # Lower bound in the cycle (min value of the lr) +step_size_multiplier: 5 #from 2 to 8 +step_size: !apply:round + - !ref * / +lr_annealing: !new:speechbrain.nnet.schedulers.CyclicLRScheduler + base_lr: !ref + max_lr: !ref + step_size: !ref +label_smoothing: 0.0 +loss: !name:speechbrain.nnet.losses.nll_loss + label_smoothing: !ref +optimizer: !name:torch.optim.Adam + lr: !ref +epoch_counter: !new:speechbrain.utils.epoch_loop.EpochCounter # epoch counter + limit: !ref +batch_size_exponent: 4 # @orion_step1: --batch_size_exponent~"uniform(4, 6,discrete=True)" +batch_size: !ref 2 ** +valid_ratio: 0.2 + +# DATA AUGMENTATION +# cutcat (disabled when min_num_segments=max_num_segments=1) +max_num_segments: 3 # @orion_step2: --max_num_segments~"uniform(2, 6, discrete=True)" +cutcat: !new:speechbrain.augment.time_domain.CutCat + min_num_segments: 2 + max_num_segments: !ref +# random amplitude gain between 0.5-1.5 uV (disabled when amp_delta=0.) +amp_delta: 0.01742 # @orion_step2: --amp_delta~"uniform(0.0, 0.5)" +rand_amp: !new:speechbrain.augment.time_domain.RandAmp + amp_low: !ref 1 - + amp_high: !ref 1 + +# random shifts between -300 ms to 300 ms (disabled when shift_delta=0.) +shift_delta_: 1 # @orion_step2: --shift_delta_~"uniform(0, 25, discrete=True)" +shift_delta: !ref 1e-2 * # 0.250 # 0.-0.25 with steps of 0.01 +min_shift: !apply:math.floor + - !ref 0 - * +max_shift: !apply:math.floor + - !ref 0 + * +time_shift: !new:speechbrain.augment.freq_domain.RandomShift + min_shift: !ref + max_shift: !ref + dim: 1 +# injection of gaussian white noise +snr_white_low: 15.0 # @orion_step2: --snr_white_low~"uniform(0.0, 15, precision=2)" +snr_white_delta: 19.1 # @orion_step2: --snr_white_delta~"uniform(5.0, 20.0, precision=3)" +snr_white_high: !ref + +add_noise_white: !new:speechbrain.augment.time_domain.AddNoise + snr_low: !ref + snr_high: !ref +position_noise_sigma: 0.01 # @orion_step1: --position_noise_sigma~"uniform(0.0, 0.1, precision=4)" + +repeat_augment: 1 # @orion_step1: --repeat_augment 0 +graph_augment: !new:torch.nn.Sequential + - !new:utils.graph_iterators.PositionNoise + sigma: !ref + # - !new:utils.graph_iterators.NodeDrop + # choose_k: 17 +augment: !new:speechbrain.augment.augmenter.Augmenter + parallel_augment: True + concat_original: True + parallel_augment_fixed_bs: True + repeat_augment: !ref + shuffle_augmentations: True + min_augmentations: 4 + max_augmentations: 4 + augmentations: + [!ref , !ref , !ref , !ref ] + +# DATA NORMALIZATION +dims_to_normalize: 1 # 1 (time) or 2 (EEG channels) +normalize: !name:speechbrain.processing.signal_processing.mean_std_norm + dims: !ref +pos_normalize: True + +# MODEL +projection_dim: 22 # @orion_step1: --projection_dim~"uniform(4, 44, discrete=True)" +spatial_focus_tau: 0.3333 # @orion_step1: --spatial_focus_tau~"uniform(0.001, 1.0, precision=4)" + +input_shape: [null, !ref , !ref , null] +cnn_temporal_kernels: 61 # @orion_step1: --cnn_temporal_kernels~"uniform(32, 72,discrete=True)" +cnn_temporal_kernelsize: 51 # @orion_step1: --cnn_temporal_kernelsize~"uniform(24, 62,discrete=True)" +# depth multiplier for the spatial depthwise conv. layer +cnn_spatial_depth_multiplier: 4 # @orion_step1: --cnn_spatial_depth_multiplier~"uniform(1, 4,discrete=True)" +cnn_spatial_max_norm: 1. # kernel max-norm constaint of the spatial depthwise conv. layer +cnn_spatial_pool: 4 +cnn_septemporal_depth_multiplier: 1 # depth multiplier for the separable temporal conv. layer +cnn_septemporal_point_kernels_ratio_: 7 # @orion_step1: --cnn_septemporal_point_kernels_ratio_~"uniform(0, 8, discrete=True)" +cnn_septemporal_point_kernels_ratio: !ref / 4 +## number of temporal filters in the separable temporal conv. layer +cnn_septemporal_point_kernels_: !ref * * +cnn_septemporal_point_kernels: !apply:math.ceil + - !ref * + 1 +cnn_septemporal_kernelsize_: 15 # @orion_step1: --cnn_septemporal_kernelsize_~"uniform(3, 24,discrete=True)" +max_cnn_spatial_pool: 4 +cnn_septemporal_kernelsize: !apply:round + - !ref * / +cnn_septemporal_pool: 7 # @orion_step1: --cnn_septemporal_pool~"uniform(1, 8,discrete=True)" +cnn_pool_type: "avg" +dense_max_norm: 0.25 # kernel max-norm constaint of the dense layer +dropout: 0.008464 # @orion_step1: --dropout~"uniform(0.0, 0.5)" +activation_type: "elu" + +model: !new:models.SpatialEEGNet.SpatialEEGNet + input_shape: !ref + cnn_temporal_kernels: !ref + cnn_temporal_kernelsize: [!ref , 1] + cnn_spatial_depth_multiplier: !ref + cnn_spatial_max_norm: !ref + cnn_spatial_pool: [!ref , 1] + cnn_septemporal_depth_multiplier: !ref + cnn_septemporal_point_kernels: !ref + cnn_septemporal_kernelsize: [!ref , 1] + cnn_septemporal_pool: [!ref , 1] + cnn_pool_type: !ref + activation_type: !ref + spatial_focus: !new:models.SpatialEEGNet.SpatialFocus + projection_dim: !ref + position_dim: 3 + tau: !ref + dense_max_norm: !ref + dropout: !ref + dense_n_neurons: !ref diff --git a/benchmarks/MOABB/hparams/P300/BNCI2014009/SpatialEEGNet_single.yaml b/benchmarks/MOABB/hparams/P300/BNCI2014009/SpatialEEGNet_single.yaml new file mode 100644 index 000000000..5e70ed446 --- /dev/null +++ b/benchmarks/MOABB/hparams/P300/BNCI2014009/SpatialEEGNet_single.yaml @@ -0,0 +1,176 @@ +seed: 1234 +__set_torchseed: !apply:torch.manual_seed [!ref ] + +# DIRECTORIES +data_folder: !PLACEHOLDER #'/path/to/dataset'. The dataset will be automatically downloaded in this folder +cached_data_folder: !PLACEHOLDER #'/path/to/pickled/dataset' +output_folder: !PLACEHOLDER #'path/to/results' + +# DATASET HPARS +# Defining the MOABB dataset. +dataset: !new:moabb.datasets.BNCI2014009 +save_prepared_dataset: True # set to True if you want to save the prepared dataset as a pkl file to load and use afterwards +data_iterator_name: !PLACEHOLDER +target_subject_idx: !PLACEHOLDER +target_session_idx: !PLACEHOLDER +events_to_load: null # all events will be loaded +original_sample_rate: 256 # Original sampling rate provided by dataset authors +sample_rate: 128 # Target sampling rate (Hz) +# band-pass filtering cut-off frequencies +fmin: 0.12 # @orion_step1: --fmin~"uniform(0.1, 5, precision=2)" +fmax: 43.3 # @orion_step1: --fmax~"uniform(20.0, 50.0, precision=3)" +n_classes: 2 +# tmin, tmax respect to stimulus onset that define the interval attribute of the dataset class +# trial begins (0 s), cue (2 s, 1.25 s long); each trial is 6 s long +# dataset interval starts from 2 +# -->tmin tmax are referred to this start value (e.g., tmin=0.5 corresponds to 2.5 s) +tmin: 0. +tmax: 0.8 +# number of steps used when selecting adjacent channels from a seed channel (default at Cz) +n_steps_channel_selection: 3 # @orion_step1: --n_steps_channel_selection~"uniform(1, 3,discrete=True)" +T: !apply:math.ceil + - !ref * ( - ) +C: 16 +# We here specify how to perfom test: +# - If test_with: 'last' we perform test with the latest model. +# - if test_with: 'best, we perform test with the best model (according to the metric specified in test_key) +# The variable avg_models can be used to average the parameters of the last (or best) N saved models before testing. +# This can have a regularization effect. If avg_models: 1, the last (or best) model is used directly. +test_with: 'last' # 'last' or 'best' +test_key: "f1" # Possible opts: "loss", "f1", "auc", "acc" + +# METRICS +f1: !name:sklearn.metrics.f1_score + pos_label: 1 +acc: !name:sklearn.metrics.balanced_accuracy_score +cm: !name:sklearn.metrics.confusion_matrix +metrics: + f1: !ref + acc: !ref + # cm: !ref +# TRAINING HPARS +n_train_examples: 100 # it will be replaced in the train script +# checkpoints to average +avg_models: 11 # @orion_step1: --avg_models~"uniform(1, 15,discrete=True)" +number_of_epochs: 894 # @orion_step1: --number_of_epochs~"uniform(250, 1000, discrete=True)" +lr: 0.0001 # @orion_step1: --lr~"choices([0.01, 0.005, 0.001, 0.0005, 0.0001])" +# Learning rate scheduling (cyclic learning rate is used here) +max_lr: !ref # Upper bound of the cycle (max value of the lr) +base_lr: 0.00000001 # Lower bound in the cycle (min value of the lr) +step_size_multiplier: 5 #from 2 to 8 +step_size: !apply:round + - !ref * / +lr_annealing: !new:speechbrain.nnet.schedulers.CyclicLRScheduler + base_lr: !ref + max_lr: !ref + step_size: !ref +label_smoothing: 0.0 +loss: !name:speechbrain.nnet.losses.nll_loss + label_smoothing: !ref +optimizer: !name:torch.optim.Adam + lr: !ref +epoch_counter: !new:speechbrain.utils.epoch_loop.EpochCounter # epoch counter + limit: !ref +batch_size_exponent: 6 # @orion_step1: --batch_size_exponent~"uniform(4, 6,discrete=True)" +batch_size: !ref 2 ** +valid_ratio: 0.2 + +# DATA AUGMENTATION +# cutcat (disabled when min_num_segments=max_num_segments=1) +max_num_segments: 2 # @orion_step2: --max_num_segments~"uniform(2, 6, discrete=True)" +cutcat: !new:speechbrain.augment.time_domain.CutCat + min_num_segments: 2 + max_num_segments: !ref +# random amplitude gain between 0.5-1.5 uV (disabled when amp_delta=0.) +amp_delta: 0.05772 # @orion_step2: --amp_delta~"uniform(0.0, 0.5)" +rand_amp: !new:speechbrain.augment.time_domain.RandAmp + amp_low: !ref 1 - + amp_high: !ref 1 + +# random shifts between -300 ms to 300 ms (disabled when shift_delta=0.) +shift_delta_: 0 # orion_step2: --shift_delta_~"uniform(0, 25, discrete=True)" +shift_delta: !ref 1e-2 * # 0.250 # 0.-0.25 with steps of 0.01 +min_shift: !apply:math.floor + - !ref 0 - * +max_shift: !apply:math.floor + - !ref 0 + * +time_shift: !new:speechbrain.augment.freq_domain.RandomShift + min_shift: !ref + max_shift: !ref + dim: 1 +# injection of gaussian white noise +snr_white_low: 5.4 # @orion_step2: --snr_white_low~"uniform(0.0, 15, precision=2)" +snr_white_delta: 16.8 # @orion_step2: --snr_white_delta~"uniform(5.0, 20.0, precision=3)" +snr_white_high: !ref + +add_noise_white: !new:speechbrain.augment.time_domain.AddNoise + snr_low: !ref + snr_high: !ref + +repeat_augment: 1 # @orion_step1: --repeat_augment 0 +augment: !new:speechbrain.augment.augmenter.Augmenter + parallel_augment: True + concat_original: True + parallel_augment_fixed_bs: True + repeat_augment: !ref + shuffle_augmentations: True + min_augmentations: 4 + max_augmentations: 4 + augmentations: [ + !ref , + !ref , + !ref , + !ref ] + +# DATA NORMALIZATION +dims_to_normalize: 1 # 1 (time) or 2 (EEG channels) +normalize: !name:speechbrain.processing.signal_processing.mean_std_norm + dims: !ref +pos_normalize: True + +# MODEL +projection_dim: 22 # @orion_step1: --projection_dim~"uniform(4, 44, discrete=True)" +spatial_focus_tau: 0.3333 # @orion_step1: --spatial_focus_tau~"uniform(0.001, 1.0, precision=4)" + +input_shape: [null, !ref , !ref , null] +cnn_temporal_kernels: 61 # @orion_step1: --cnn_temporal_kernels~"uniform(32, 72,discrete=True)" +cnn_temporal_kernelsize: 51 # @orion_step1: --cnn_temporal_kernelsize~"uniform(24, 62,discrete=True)" +# depth multiplier for the spatial depthwise conv. layer +cnn_spatial_depth_multiplier: 4 # @orion_step1: --cnn_spatial_depth_multiplier~"uniform(1, 4,discrete=True)" +cnn_spatial_max_norm: 1. # kernel max-norm constaint of the spatial depthwise conv. layer +cnn_spatial_pool: 4 +cnn_septemporal_depth_multiplier: 1 # depth multiplier for the separable temporal conv. layer +cnn_septemporal_point_kernels_ratio_: 7 # @orion_step1: --cnn_septemporal_point_kernels_ratio_~"uniform(0, 8, discrete=True)" +cnn_septemporal_point_kernels_ratio: !ref / 4 +## number of temporal filters in the separable temporal conv. layer +cnn_septemporal_point_kernels_: !ref * * +cnn_septemporal_point_kernels: !apply:math.ceil + - !ref * + 1 +cnn_septemporal_kernelsize_: 15 # @orion_step1: --cnn_septemporal_kernelsize_~"uniform(3, 24,discrete=True)" +max_cnn_spatial_pool: 4 +cnn_septemporal_kernelsize: !apply:round + - !ref * / +cnn_septemporal_pool: 7 # @orion_step1: --cnn_septemporal_pool~"uniform(1, 8,discrete=True)" +cnn_pool_type: "avg" +dense_max_norm: 0.25 # kernel max-norm constaint of the dense layer +dropout: 0.008464 # @orion_step1: --dropout~"uniform(0.0, 0.5)" +activation_type: "elu" + +model: !new:models.SpatialEEGNet.SpatialEEGNet + input_shape: !ref + cnn_temporal_kernels: !ref + cnn_temporal_kernelsize: [!ref , 1] + cnn_spatial_depth_multiplier: !ref + cnn_spatial_max_norm: !ref + cnn_spatial_pool: [!ref , 1] + cnn_septemporal_depth_multiplier: !ref + cnn_septemporal_point_kernels: !ref + cnn_septemporal_kernelsize: [!ref , 1] + cnn_septemporal_pool: [!ref , 1] + cnn_pool_type: !ref + activation_type: !ref + spatial_focus: !new:models.SpatialEEGNet.SpatialFocus + projection_dim: !ref + position_dim: 3 + tau: !ref + dense_max_norm: !ref + dropout: !ref + dense_n_neurons: !ref diff --git a/benchmarks/MOABB/models/SpatialEEGNet.py b/benchmarks/MOABB/models/SpatialEEGNet.py new file mode 100644 index 000000000..285d88d38 --- /dev/null +++ b/benchmarks/MOABB/models/SpatialEEGNet.py @@ -0,0 +1,312 @@ +"""EEGNet adapted to handle variable electrode configurations. + +Base EEGNet implementation borrowed from Davide Borra's implementation in Speechbrain/benchmarks: + + EEGNet from https://doi.org/10.1088/1741-2552/aace8c. + Shallow and lightweight convolutional neural network proposed for a general decoding of single-trial EEG signals. + It was proposed for P300, error-related negativity, motor execution, motor imagery decoding. + +Authors + * Drew Wagner, 2024 +""" + +import speechbrain as sb +import torch +import torch.nn as nn +import torch_geometric.utils +from torch_geometric.data import Data + + +class node_wise(nn.Module): + def __init__(self, func): + super().__init__() + self.func = func + + def forward(self, x: Data): + new_x = x.clone() + new_x.x = self.func(x.x.unsqueeze(-1).unsqueeze(-1)).squeeze(-2) + + return new_x + + +class SpatialFocus(nn.Module): + def __init__(self, projection_dim, position_dim=3, tau=1.0): + super().__init__() + self.projection_dim = projection_dim + self.position_dim = position_dim + self.tau = tau + + self.similarity_module = nn.Sequential( + nn.Linear(position_dim, projection_dim), + nn.ELU(), + nn.Linear(projection_dim, projection_dim), + ) + self.softmax = nn.Softmax(dim=-2) + + def forward(self, batch: Data): + positions = batch.pos + x = batch.x + + weights = self.similarity_module(positions).div_(self.tau) + weights = ( + torch_geometric.utils.softmax(weights, index=batch.batch) + .view(weights.shape[0], 1, weights.shape[1], 1) + ) + x = x.unsqueeze(-2) * weights + x = torch_geometric.utils.scatter( + x, + batch.batch, + dim=0, + dim_size=batch.batch_size, + reduce="sum", + ) + return x + + +class SpatialEEGNet(nn.Module): + """EEGNet. + + Arguments + --------- + input_shape: tuple + The shape of the input. + cnn_temporal_kernels: int + Number of kernels in the 2d temporal convolution. + cnn_temporal_kernelsize: tuple + Kernel size of the 2d temporal convolution. + cnn_spatial_depth_multiplier: int + Depth multiplier of the 2d spatial depthwise convolution. + cnn_spatial_max_norm: float + Kernel max norm of the 2d spatial depthwise convolution. + cnn_spatial_pool: tuple + Pool size and stride after the 2d spatial depthwise convolution. + cnn_septemporal_depth_multiplier: int + Depth multiplier of the 2d temporal separable convolution. + cnn_septemporal_kernelsize: tuple + Kernel size of the 2d temporal separable convolution. + cnn_septemporal_pool: tuple + Pool size and stride after the 2d temporal separable convolution. + cnn_pool_type: string + Pooling type. + dropout: float + Dropout probability. + dense_max_norm: float + Weight max norm of the fully-connected layer. + dense_n_neurons: int + Number of output neurons. + activation_type: str + Activation function of the hidden layers. + + Example + ------- + #>>> inp_tensor = torch.rand([1, 200, 32, 1]) + #>>> model = EEGNet(input_shape=inp_tensor.shape) + #>>> output = model(inp_tensor) + #>>> output.shape + #torch.Size([1,4]) + """ + + def __init__( + self, + spatial_focus, + input_shape=None, # (1, T, C, 1) + cnn_temporal_kernels=8, + cnn_temporal_kernelsize=(33, 1), + cnn_spatial_depth_multiplier=2, + cnn_spatial_max_norm=1.0, + cnn_spatial_pool=(4, 1), + cnn_septemporal_depth_multiplier=1, + cnn_septemporal_point_kernels=None, + cnn_septemporal_kernelsize=(17, 1), + cnn_septemporal_pool=(8, 1), + cnn_pool_type="avg", + dropout=0.5, + dense_max_norm=0.25, + dense_n_neurons=4, + activation_type="elu", + ): + super().__init__() + if input_shape is None: + raise ValueError("Must specify input_shape") + if activation_type == "gelu": + activation = nn.GELU() + elif activation_type == "elu": + activation = nn.ELU() + elif activation_type == "relu": + activation = nn.ReLU() + elif activation_type == "leaky_relu": + activation = nn.LeakyReLU() + elif activation_type == "prelu": + activation = nn.PReLU() + else: + raise ValueError("Wrong hidden activation function") + self.default_sf = 128 # sampling rate of the original publication (Hz) + # T = input_shape[1] + C = input_shape[2] + + # CONVOLUTIONAL MODULE + self.temporal_frontend = nn.Sequential() + # Temporal convolution + self.temporal_frontend.add_module( + "conv_0", + sb.nnet.CNN.Conv2d( + in_channels=1, + out_channels=cnn_temporal_kernels, + kernel_size=cnn_temporal_kernelsize, + padding="same", + padding_mode="constant", + bias=False, + swap=True, + ), + ) + self.temporal_frontend.add_module( + "bnorm_0", + sb.nnet.normalization.BatchNorm2d( + input_size=cnn_temporal_kernels, + momentum=0.01, + affine=True, + ), + ) + self.spatial_focus = spatial_focus + self.conv_module = nn.Sequential() + # Spatial depthwise convolution + cnn_spatial_kernels = ( + cnn_spatial_depth_multiplier * cnn_temporal_kernels + ) + self.conv_module.add_module( + "conv_1", + sb.nnet.CNN.Conv2d( + in_channels=cnn_temporal_kernels, + out_channels=cnn_spatial_kernels, + kernel_size=(1, C), + groups=cnn_temporal_kernels, + padding="valid", + bias=False, + max_norm=cnn_spatial_max_norm, + swap=True, + ), + ) + self.conv_module.add_module( + "bnorm_1", + sb.nnet.normalization.BatchNorm2d( + input_size=cnn_spatial_kernels, + momentum=0.01, + affine=True, + ), + ) + self.conv_module.add_module("act_1", activation) + self.conv_module.add_module( + "pool_1", + sb.nnet.pooling.Pooling2d( + pool_type=cnn_pool_type, + kernel_size=cnn_spatial_pool, + stride=cnn_spatial_pool, + pool_axis=[1, 2], + ), + ) + self.conv_module.add_module("dropout_1", nn.Dropout(p=dropout)) + + # Temporal separable convolution + cnn_septemporal_kernels = ( + cnn_spatial_kernels * cnn_septemporal_depth_multiplier + ) + self.conv_module.add_module( + "conv_2", + sb.nnet.CNN.Conv2d( + in_channels=cnn_spatial_kernels, + out_channels=cnn_septemporal_kernels, + kernel_size=cnn_septemporal_kernelsize, + groups=cnn_spatial_kernels, + padding="same", + padding_mode="constant", + bias=False, + swap=True, + ), + ) + + if cnn_septemporal_point_kernels is None: + cnn_septemporal_point_kernels = cnn_septemporal_kernels + + self.conv_module.add_module( + "conv_3", + sb.nnet.CNN.Conv2d( + in_channels=cnn_septemporal_kernels, + out_channels=cnn_septemporal_point_kernels, + kernel_size=(1, 1), + padding="valid", + bias=False, + swap=True, + ), + ) + self.conv_module.add_module( + "bnorm_3", + sb.nnet.normalization.BatchNorm2d( + input_size=cnn_septemporal_point_kernels, + momentum=0.01, + affine=True, + ), + ) + self.conv_module.add_module("act_3", activation) + self.conv_module.add_module( + "pool_3", + sb.nnet.pooling.Pooling2d( + pool_type=cnn_pool_type, + kernel_size=cnn_septemporal_pool, + stride=cnn_septemporal_pool, + pool_axis=[1, 2], + ), + ) + self.conv_module.add_module("dropout_3", nn.Dropout(p=dropout)) + + # Shape of intermediate feature maps + dense_input_size = self._num_flat_features(input_shape) + # DENSE MODULE + self.dense_module = nn.Sequential() + self.dense_module.add_module( + "flatten", + nn.Flatten(), + ) + self.dense_module.add_module( + "fc_out", + sb.nnet.linear.Linear( + input_size=dense_input_size, + n_neurons=dense_n_neurons, + max_norm=dense_max_norm, + ), + ) + self.dense_module.add_module("act_out", nn.LogSoftmax(dim=1)) + + def _num_flat_features(self, input_shape): + """Returns the number of flattened features from a tensor. + + Arguments + --------- + x : torch.Tensor + Input feature map. + """ + x = self.temporal_frontend( + torch.ones((1,) + tuple(input_shape[1:-1]) + (1,)) + ) + # x = self.spatial_focus(x) + x = self.conv_module(x) + size = x.size()[1:] # all dimensions except the batch dimension + num_features = 1 + for s in size: + num_features *= s + return num_features + + def forward(self, x): + """Returns the output of the model. + + Arguments + --------- + x : torch_geometric.Data (batch, time, EEG channel, channel) + Input to convolve. 4d tensors are expected. + """ + # x is initially (b*c, t) + x = node_wise(self.temporal_frontend)(x) # returns as Data (b*c, t', d) + x = self.spatial_focus(x) # returns as Tensor (b, t', c', d) + x = self.conv_module(x) # returns as Tensor (b, t'', 1, d') + x = self.dense_module(x) + + return x diff --git a/benchmarks/MOABB/multi_eval.sh b/benchmarks/MOABB/multi_eval.sh new file mode 100755 index 000000000..a6f7669c0 --- /dev/null +++ b/benchmarks/MOABB/multi_eval.sh @@ -0,0 +1,20 @@ +#!/bin/bash + +# Define the array of numbers +numbers=(17 13 11 9 4) + +set -e +# Iterate over the array +for k in "${numbers[@]}"; do + # Execute the run_experiments.sh script with the specified parameters + ./run_experiments.sh --hparams "hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml" \ + --data_folder "~/mne_data" \ + --cached_data_folder "~/mne_data/pkl" \ + --output_folder "results/MotorImagery/BNCI2014001/SpatialEEGNet/eval/choose_k$k" \ + --nsbj 9 \ + --nsess 2 \ + --nruns 10 \ + --train_mode "leave-one-session-out" \ + --channel_drop_choose_k "$k" \ + --device "cuda" +done diff --git a/benchmarks/MOABB/run_experiments.sh b/benchmarks/MOABB/run_experiments.sh index 37af36f9a..1ae135897 100755 --- a/benchmarks/MOABB/run_experiments.sh +++ b/benchmarks/MOABB/run_experiments.sh @@ -225,6 +225,7 @@ for i in $(seq 0 1 $(( nruns - 1 ))); do elif [ "$train_mode" = "leave-one-session-out" ]; then # Loop over sessions + set -e for j in $(seq 0 1 $(( nsess - 1 ))); do run_experiment $j $output_folder_exp done diff --git a/benchmarks/MOABB/scripts/slurm_cp_dataset.sh b/benchmarks/MOABB/scripts/slurm_cp_dataset.sh new file mode 100755 index 000000000..b4a99673e --- /dev/null +++ b/benchmarks/MOABB/scripts/slurm_cp_dataset.sh @@ -0,0 +1,2 @@ +mkdir -p $SLURM_TMPDIR/mne_data +tar -axf $HOME/projects/def-ravanelm/datasets-open/$1.tar -C $SLURM_TMPDIR/mne_data diff --git a/benchmarks/MOABB/scripts/slurm_hparam_opt.sh b/benchmarks/MOABB/scripts/slurm_hparam_opt.sh new file mode 100755 index 000000000..db758809d --- /dev/null +++ b/benchmarks/MOABB/scripts/slurm_hparam_opt.sh @@ -0,0 +1,14 @@ +#!/bin/sh +$HOME/speechbrain-benchmarks-private/benchmarks/MOABB/scripts/slurm_install_env.sh +module load StdEnv/2020 +module load python/3.10.2 +module load scipy-stack + +source $SLURM_TMPDIR/venv/bin/activate +$HOME/speechbrain-benchmarks-private/benchmarks/MOABB/scripts/slurm_cp_dataset.sh $DATASET_TAG + +cd $HOME/speechbrain-benchmarks-private/benchmarks/MOABB +./run_hparam_optimization.sh $@ \ + --output_folder $SLURM_TMPDIR/output_$SLURM_JOBID \ + --data_folder $SLURM_TMPDIR/mne_data/ +tar -caf $OUTPUT_FOLDER/results.tar.gz -C $SLURM_TMPDIR $SLURM_TMPDIR/output_$SLURM_JOBID diff --git a/benchmarks/MOABB/scripts/slurm_install_env.sh b/benchmarks/MOABB/scripts/slurm_install_env.sh new file mode 100755 index 000000000..237f2fe37 --- /dev/null +++ b/benchmarks/MOABB/scripts/slurm_install_env.sh @@ -0,0 +1,18 @@ +module load StdEnv/2020 +module load python/3.10.2 +module load scipy-stack + +virtualenv --no-download $SLURM_TMPDIR/venv +source $SLURM_TMPDIR/venv/bin/activate + +# NOTE: The order of installs is important! +pip install --no-index scikit-learn orion torch_geometric torchinfo +pip install --no-index $SCRATCH/wheels/EDFlib_Python-1.0.8-py3-none-any.whl +pip install --no-index $SCRATCH/wheels/edfio-0.4.3-py3-none-any.whl +pip install --no-index $SCRATCH/wheels/mne-1.6.1-py3-none-any.whl +pip install --no-index $SCRATCH/wheels/mne_bids-0.13-py2.py3-none-any.whl +pip install --no-index $SCRATCH/wheels/pymatreader-0.0.32-py3-none-any.whl +pip install --no-index $SCRATCH/wheels/pyriemann-0.5-py2.py3-none-any.whl +pip install --no-index $SCRATCH/wheels/speechbrain-1.0.0-py3-none-any.whl +pip install --no-index $SCRATCH/wheels/memory_profiler-0.61.0-py3-none-any.whl +pip install --no-index $SCRATCH/wheels/moabb-1.0.0-py3-none-any.whl diff --git a/benchmarks/MOABB/scripts/slurm_train_base.sh b/benchmarks/MOABB/scripts/slurm_train_base.sh new file mode 100755 index 000000000..166038b26 --- /dev/null +++ b/benchmarks/MOABB/scripts/slurm_train_base.sh @@ -0,0 +1,5 @@ +source $HOME/speechbrain-benchmarks-private/benchmarks/MOABB/scripts/slurm_install_env.sh +$HOME/speechbrain-benchmarks-private/benchmarks/MOABB/scripts/slurm_cp_dataset.sh $dataset_tag + +cd $HOME/speechbrain-benchmarks-private/benchmarks/MOABB +python train.py $@ --data_folder=$SLURM_TMPDIR/mne_data --cached_data_folder=$SLURM_TMPDIR/mne_data/pkl diff --git a/benchmarks/MOABB/train.py b/benchmarks/MOABB/train.py index 78d3fa058..fd8ae7438 100644 --- a/benchmarks/MOABB/train.py +++ b/benchmarks/MOABB/train.py @@ -10,20 +10,23 @@ ------ Davide Borra, 2022 Mirco Ravanelli, 2023 +Drew Wagner, 2024 """ -import pickle -import os -import torch -from hyperpyyaml import load_hyperpyyaml -from torch.nn import init -import numpy as np import logging +import os +import pickle import sys -from utils.dataio_iterators import LeaveOneSessionOut, LeaveOneSubjectOut -from torchinfo import summary + +import numpy as np import speechbrain as sb +import torch import yaml +from hyperpyyaml import load_hyperpyyaml +from torch.nn import init +from torch_geometric.data import Batch + +from utils.graph_iterators import LeaveOneSessionOut, LeaveOneSubjectOut class MOABBBrain(sb.Brain): @@ -41,24 +44,47 @@ def init_model(self, model): def compute_forward(self, batch, stage): "Given an input batch it computes the model output." - inputs = batch[0].to(self.device) + inputs = batch.to(self.device) # Perform data augmentation - if stage == sb.Stage.TRAIN and hasattr(self.hparams, "augment"): - inputs, _ = self.hparams.augment( - inputs.squeeze(3), - lengths=torch.ones(inputs.shape[0], device=self.device), + if stage == sb.Stage.TRAIN and hasattr(self.hparams, "augment") and self.hparams.repeat_augment > 0: + # Force concat_original to be false, so we can manually concat the original + self.hparams.augment.concat_original = False + aug, _ = self.hparams.augment( + inputs.x.unsqueeze(-1), + lengths=torch.ones(inputs.x.shape[0], device=self.device), ) - inputs = inputs.unsqueeze(3) + aug = aug.squeeze(-1) + + assert aug.shape == inputs.x.shape, f"Shape changed during augmentation: {aug.shape} != {inputs.x.shape}" + inputs_aug = inputs.clone() + inputs_aug.x = aug.squeeze(-1) + inputs = inputs.concat(inputs_aug) + + if hasattr(self.hparams, "pos_normalize"): + if self.hparams.pos_normalize is True: + inputs.pos = 2 * ( + (inputs.pos - inputs.pos.min(dim=0).values) + / ( + inputs.pos.max(dim=0).values + - inputs.pos.min(dim=0).values + ) + - 0.5 + ) + elif self.hparams.pos_normalize is not False: + inputs.pos = self.hparams.pos_normalize(inputs.pos) + + if stage == sb.Stage.TRAIN and hasattr(self.hparams, "graph_augment"): + inputs = self.hparams.graph_augment(inputs) # Normalization if hasattr(self.hparams, "normalize"): - inputs = self.hparams.normalize(inputs) + inputs.x = self.hparams.normalize(inputs.x) return self.modules.model(inputs) def compute_objectives(self, predictions, batch, stage): "Given the network predictions and targets computes the loss." - targets = batch[1].to(self.device) + targets = batch.y.to(self.device) # Target augmentation N_augments = int(predictions.shape[0] / targets.shape[0]) @@ -75,7 +101,7 @@ def compute_objectives(self, predictions, batch, stage): # From log to linear predictions tmp_preds = torch.exp(predictions) self.preds.extend(tmp_preds.detach().cpu().numpy()) - self.targets.extend(batch[1].detach().cpu().numpy()) + self.targets.extend(batch.y.cpu().numpy()) else: if hasattr(self.hparams, "lr_annealing"): self.hparams.lr_annealing.on_batch_end(self.optimizer) @@ -85,18 +111,18 @@ def on_fit_start(self,): """Gets called at the beginning of ``fit()``""" self.init_model(self.hparams.model) self.init_optimizers() - in_shape = ( - (1,) - + tuple(np.floor(self.hparams.input_shape[1:-1]).astype(int)) - + (1,) - ) - model_summary = summary( - self.hparams.model, input_size=in_shape, device=self.device - ) - with open( - os.path.join(self.hparams.exp_dir, "model.txt"), "w" - ) as text_file: - text_file.write(str(model_summary)) + # in_shape = ( + # (1,) + # + tuple(np.floor(self.hparams.input_shape[1:-1]).astype(int)) + # + (1,) + # ) + # model_summary = summary( + # self.hparams.model, input_size=in_shape, device=self.device + # ) + # with open( + # os.path.join(self.hparams.exp_dir, "model.txt"), "w" + # ) as text_file: + # text_file.write(str(model_summary)) def on_stage_start(self, stage, epoch=None): "Gets called when a stage (either training, validation, test) starts." @@ -185,12 +211,16 @@ def on_stage_end(self, stage, stage_loss, epoch=None): stats_meta={ "epoch loaded": self.hparams.epoch_counter.current }, - test_stats=self.last_eval_stats - if not getattr(self, "log_test_as_valid", False) - else None, - valid_stats=self.last_eval_stats - if getattr(self, "log_test_as_valid", False) - else None, + test_stats=( + self.last_eval_stats + if not getattr(self, "log_test_as_valid", False) + else None + ), + valid_stats=( + self.last_eval_stats + if getattr(self, "log_test_as_valid", False) + else None + ), ) # save the averaged checkpoint at the end of the evaluation stage # delete the rest of the intermediate checkpoints @@ -228,7 +258,7 @@ def check_if_best( self, last_eval_stats, best_eval_stats, keys, ): """Checks if the current model is the best according at least to - one of the monitored metrics. """ + one of the monitored metrics.""" is_best = False for key in keys: if key == "loss": @@ -246,11 +276,11 @@ def check_if_best( def run_experiment(hparams, run_opts, datasets): """This function performs a single training (e.g., single cross-validation fold)""" - idx_examples = np.arange(datasets["train"].dataset.tensors[0].shape[0]) + ys = Batch.from_data_list(datasets["train"].dataset).y + + idx_examples = np.arange(ys.shape[0]) n_examples_perclass = [ - idx_examples[ - np.where(datasets["train"].dataset.tensors[1] == c)[0] - ].shape[0] + idx_examples[np.where(ys == c)[0]].shape[0] for c in range(hparams["n_classes"]) ] n_examples_perclass = np.array(n_examples_perclass) @@ -269,22 +299,6 @@ def run_experiment(hparams, run_opts, datasets): ) logger = logging.getLogger(__name__) logger.info("Experiment directory: {0}".format(hparams["exp_dir"])) - logger.info( - "Input shape: {0}".format( - datasets["train"].dataset.tensors[0].shape[1:] - ) - ) - logger.info( - "Training set avg value: {0}".format( - datasets["train"].dataset.tensors[0].mean() - ) - ) - datasets_summary = "Number of examples: {0} (training), {1} (validation), {2} (test)".format( - datasets["train"].dataset.tensors[0].shape[0], - datasets["valid"].dataset.tensors[0].shape[0], - datasets["test"].dataset.tensors[0].shape[0], - ) - logger.info(datasets_summary) brain = MOABBBrain( modules={"model": hparams["model"]}, @@ -339,36 +353,44 @@ def prepare_dataset_iterators(hparams): # defining data iterator to use print("Prepare dataset iterators...") if hparams["data_iterator_name"] == "leave-one-session-out": - data_iterator = LeaveOneSessionOut( - seed=hparams["seed"] - ) # within-subject and cross-session + DataIterator = LeaveOneSessionOut elif hparams["data_iterator_name"] == "leave-one-subject-out": - data_iterator = LeaveOneSubjectOut( - seed=hparams["seed"] - ) # cross-subject and cross-session + DataIterator = LeaveOneSubjectOut else: raise ValueError( "Unknown data_iterator_name: %s" % hparams["data_iterator_name"] ) - - tail_path, datasets = data_iterator.prepare( - data_folder=hparams["data_folder"], - dataset=hparams["dataset"], - cached_data_folder=hparams["cached_data_folder"], - batch_size=hparams["batch_size"], - valid_ratio=hparams["valid_ratio"], - target_subject_idx=hparams["target_subject_idx"], - target_session_idx=hparams["target_session_idx"], - events_to_load=hparams["events_to_load"], - original_sample_rate=hparams["original_sample_rate"], - sample_rate=hparams["sample_rate"], + + data_iterator = DataIterator( + datasets=hparams["datasets"], + resample=hparams["sample_rate"], fmin=hparams["fmin"], fmax=hparams["fmax"], tmin=hparams["tmin"], tmax=hparams["tmax"], - save_prepared_dataset=hparams["save_prepared_dataset"], - n_steps_channel_selection=hparams["n_steps_channel_selection"], + events=hparams["events_to_load"], + valid_ratio=hparams["valid_ratio"], + target_subjects=hparams["target_subject_idx"] + 1, + target_sessions=hparams["target_session_idx"], ) + + datasets = data_iterator.prepare( + data_folder=hparams["data_folder"], + cached_data_folder=hparams["cached_data_folder"], + batch_size=hparams["batch_size"], + ) + + tail_path = os.path.join( + hparams["data_iterator_name"], + "_".join( + "sub-{0}".format(str(subject).zfill(3)) + for subject in data_iterator.target_subjects + ), + ) + if data_iterator.target_session_names is not None: + tail_path = os.path.join( + tail_path, "_".join(map(str, data_iterator.target_session_names)) + ) return tail_path, datasets @@ -382,9 +404,9 @@ def load_hparams_and_dataset_iterators(hparams_file, run_opts, overrides): tail_path, datasets = prepare_dataset_iterators(hparams) # override C and T, to be sure that network input shape matches the dataset (e.g., after time cropping or channel sampling) overrides.update( - T=datasets["train"].dataset.tensors[0].shape[1], - C=datasets["train"].dataset.tensors[0].shape[-2], - n_train_examples=datasets["train"].dataset.tensors[0].shape[0], + T=datasets["T"], + C=datasets["C"], + n_train_examples=len(datasets["train"].dataset), ) # loading hparams for the each training and evaluation processes diff --git a/benchmarks/MOABB/utils/dataio_iterators.py b/benchmarks/MOABB/utils/dataio_iterators.py index 07379f198..231a73cad 100644 --- a/benchmarks/MOABB/utils/dataio_iterators.py +++ b/benchmarks/MOABB/utils/dataio_iterators.py @@ -6,18 +6,35 @@ - Leave-One-Session-Out - Leave-One-Subject-Out -Author +Authors ------ Davide Borra, 2021 +Drew Wagner, 2024 """ +import abc +import os +from collections import namedtuple +from typing import Dict, List, Optional, Protocol, Tuple, TypedDict + import numpy as np +import pandas as pd import torch -from torch.utils.data import TensorDataset, DataLoader -import os +import torch_geometric.data +import torch_geometric.loader +from moabb.datasets.base import BaseDataset as MOABBDataset +from torch.utils.data import DataLoader, IterableDataset, TensorDataset from utils.prepare import prepare_data +class _IterableDatasetFromGenerator(IterableDataset): + def __init__(self, gen): + self.gen = gen + + def __iter__(self): + return iter(self.gen) + + def get_idx_train_valid_classbalanced(idx_train, valid_ratio, y): """This function returns train and valid indices balanced across classes.""" idx_train = np.array(idx_train) @@ -119,51 +136,94 @@ def sample_channels(x, adjacency_mtx, ch_names, n_steps, seed_nodes=["Cz"]): if idx_sel_channels.shape[0] != x.shape[1]: x = x[:, idx_sel_channels, :] - sel_channels_ = np.array(ch_names)[idx_sel_channels] - sel_channels_ = list(sel_channels_) + ch_names = np.array(ch_names)[idx_sel_channels] + ch_names = list(ch_names) # should correspond to sel_channels ordered based on ch_names ordering - print("Sampling channels: {0}".format(sel_channels_)) + print("Sampling channels: {0}".format(ch_names)) else: print("Sampling all channels available: {0}".format(ch_names)) - return x + return x, idx_sel_channels, ch_names -class LeaveOneSessionOut(object): - """Leave one session out iterator for MOABB datasets. - Designing within-subject, cross-session and session-agnostic iterations on the dataset for a specific paradigm. - For each subject, one session is held back as test set and the remaining ones are used to train neural networks. - The validation set can be sampled from a separate (and held back) session if enough sessions are available; otherwise, the validation set is sampled from the training set. - All sets are extracted balanced across subjects, sessions and classes. +def adjacency_mtx_to_edge_list(adjacency_mtx): + """Convert adjacency matrix to edge list format""" + edge_list = np.vstack(np.nonzero(adjacency_mtx)) + return torch.tensor(edge_list, dtype=torch.long).contiguous() - Arguments - --------- - seed: int - Seed for random number generators. - """ - def __init__(self, seed): - self.iterator_tag = "leave-one-session-out" +class _SplitDataloaders(TypedDict): + train: DataLoader + valid: DataLoader + test: DataLoader + ch_names: List[str] + ch_positions: np.ndarray + adjacency_mtx: np.ndarray + max_T: int + + +XYSplits = namedtuple( + "XYSplits", ["x_train", "y_train", "x_valid", "y_valid", "x_test", "y_test"] +) + + +class _DataDict(TypedDict): + srate: int + original_interval: Tuple[float, float] + adjacency_mtx: np.ndarray + ch_positions: Dict[str, List[float]] + channels: List[str] + subject: str + + +class DataLoaderFactory(Protocol): + def prepare( + self, + *, + data_folder: str, + cached_data_folder: str, + dataset: MOABBDataset, + batch_size: int, + valid_ratio: float, + original_sample_rate: int, + target_subject_idx: int, + target_session_idx: Optional[int] = None, + sample_rate: Optional[int] = None, + fmin: Optional[float] = None, + fmax: Optional[float] = None, + tmin: Optional[float] = None, + tmax: Optional[float] = None, + n_steps_channel_selection: Optional[int] = None, + events_to_load: Optional[List[str]] = None, + save_prepared_dataset: bool = True, + ) -> Tuple[str, _SplitDataloaders]: + ... + + +class BaseDataIOIterator(DataLoaderFactory, abc.ABC): + def __init__(self, tag, seed): + self.iterator_tag = tag np.random.seed(seed) def prepare( self, - data_folder=None, - cached_data_folder=None, - dataset=None, - batch_size=None, - valid_ratio=None, - target_subject_idx=None, - target_session_idx=None, - events_to_load=None, - original_sample_rate=None, - sample_rate=None, - fmin=None, - fmax=None, - tmin=None, - tmax=None, - save_prepared_dataset=None, - n_steps_channel_selection=None, - ): + *, + data_folder: str, + cached_data_folder: str, + dataset: MOABBDataset, + batch_size: int, + valid_ratio: float, + original_sample_rate: int, + target_subject_idx: int, + target_session_idx: Optional[int] = None, + sample_rate: Optional[int] = None, + fmin: Optional[float] = None, + fmax: Optional[float] = None, + tmin: Optional[float] = None, + tmax: Optional[float] = None, + n_steps_channel_selection: Optional[int] = None, + events_to_load: Optional[List[str]] = None, + save_prepared_dataset: bool = True, + ) -> Tuple[str, _SplitDataloaders]: """This function returns the pre-processed datasets (training, validation and test sets). Arguments @@ -180,16 +240,12 @@ def prepare( Mini-batch size. valid_ratio: float Ratio for extracting the validation set from the available training set (between 0 and 1). + original_sample_rate: int + Sampling rate of the loaded dataset (Hz). target_subject_idx: int Index of the subject to use to train the network. target_session_idx: int Index of the session to use to train the network. - events_to_load: list - List of 'events' considered when loading the MOABB dataset. - It serves to load specific conditions (e.g., ["right_hand", "left_hand"] for specific movement conditions). - See MOABB documentation and reference publications of each dataset for additional details about datasets. - original_sample_rate: int - Sampling rate of the loaded dataset (Hz). sample_rate: int Target sampling rate (Hz). fmin: float @@ -202,10 +258,15 @@ def prepare( tmax: float Stop time of the EEG epoch, with respect to the event as defined in the dataset (s). See MOABB documentation and reference publications of each dataset for additional details about datasets. - save_prepared_dataset: bool - Flag to save the prepared dataset into a pkl file. n_steps_channel_selection: int Number of steps to perform when sampling a subset of channels from a seed channel, based on the adjacency matrix. + events_to_load: list + List of 'events' considered when loading the MOABB dataset. + It serves to load specific conditions (e.g., ["right_hand", "left_hand"] for specific movement conditions). + See MOABB documentation and reference publications of each dataset for additional details about datasets. + + save_prepared_dataset: bool + Flag to save the prepared dataset into a pkl file. ... Returns @@ -213,38 +274,278 @@ def prepare( tail_path: str String containing the relative path where results will be stored for the specified iterator, subject and session. datasets: dict - Dictionary containing all sets (keys: 'train', 'test', 'valid'). - --------- + Dictionary containing all sets as dataloaders (keys: 'train', 'test', 'valid'). + --------- """ interval = [tmin, tmax] + subject = dataset.subject_list[target_subject_idx] + + ( + target_data_dict, + (x_train, y_train, x_valid, y_valid, x_test, y_test), + ) = self.get_data( + target_subject_idx=target_subject_idx, + target_session_idx=target_session_idx, + valid_ratio=valid_ratio, + dataset=dataset, + data_folder=data_folder, + cached_data_folder=cached_data_folder, + original_sample_rate=original_sample_rate, + sample_rate=sample_rate, + fmin=fmin, + fmax=fmax, + events_to_load=events_to_load, + save_prepared_dataset=save_prepared_dataset, + ) - # preparing or loading dataset - data_dict = prepare_data( + # time cropping + if interval != target_data_dict["interval"]: + x_train = crop_signals( + x=x_train, + srate=target_data_dict["srate"], + interval_in=target_data_dict["interval"], + interval_out=interval, + ) + x_valid = crop_signals( + x=x_valid, + srate=target_data_dict["srate"], + interval_in=target_data_dict["interval"], + interval_out=interval, + ) + x_test = crop_signals( + x=x_test, + srate=target_data_dict["srate"], + interval_in=target_data_dict["interval"], + interval_out=interval, + ) + + adjacency_mtx = target_data_dict["adjacency_mtx"] + ch_positions = target_data_dict["ch_positions"] + ch_names = target_data_dict["channels"] + + # channel sampling + if n_steps_channel_selection is not None: + x_train, ch_idx, ch_names_sel = sample_channels( + x_train, + adjacency_mtx, + ch_names, + n_steps=n_steps_channel_selection, + ) + x_valid, *_ = sample_channels( + x_valid, + adjacency_mtx, + ch_names, + n_steps=n_steps_channel_selection, + ) + x_test, *_ = sample_channels( + x_test, + adjacency_mtx, + ch_names, + n_steps=n_steps_channel_selection, + ) + adjacency_mtx = adjacency_mtx[ch_idx, :][:, ch_idx] + ch_names = ch_names_sel + + ch_positions = np.row_stack([ch_positions[ch] for ch in ch_names]) + + # swap axes: from (N_examples, C, T) to (N_examples, T, C) + x_train = np.swapaxes(x_train, -1, -2) + x_valid = np.swapaxes(x_valid, -1, -2) + x_test = np.swapaxes(x_test, -1, -2) + + # dataloaders + train_loader, valid_loader, test_loader = get_dataloader( + batch_size, (x_train, y_train), (x_valid, y_valid), (x_test, y_test) + ) + + datasets: _SplitDataloaders = dict( + train=train_loader, + valid=valid_loader, + test=test_loader, + ch_names=ch_names, + ch_positions=ch_positions, + adjacency_mtx=adjacency_mtx, + max_T=max(x_train.shape[1], x_valid.shape[1], x_test.shape[1]), + ) + + tail_path = self.get_tail_path(subject, target_data_dict.get("session")) + return tail_path, datasets + + @abc.abstractmethod + def get_data( + self, + target_subject_idx: int, + target_session_idx: Optional[int], + valid_ratio: float, + dataset: MOABBDataset, + data_folder: Optional[str], + cached_data_folder: Optional[str], + original_sample_rate: int, + sample_rate: Optional[int], + fmin: Optional[float], + fmax: Optional[float], + events_to_load: Optional[List[str]], + save_prepared_dataset: bool, + ) -> tuple[_DataDict, XYSplits]: + """Prepare the numpy data as train, valid and test splits. + + Arguments + ========= + See `prepare` method. + + Returns + ======= + target_data_dict: _DataDict + The metadata relating to the target, including session name and + adjacency matrix. + (x_train, y_train, x_valid, y_valid, x_test, y_test): np.ndarray + The numpy arrays corresponding to each data split. + """ + + def get_tail_path(self, subject: int, session: Optional[str]) -> str: + """Returns the tail path for the directory.""" + + tail_path = os.path.join( + self.iterator_tag, "sub-{0}".format(str(subject).zfill(3)), + ) + if session is not None: + tail_path = os.path.join(tail_path, session) + return tail_path + + +class AsGraph(DataLoaderFactory): + def __init__(self, inner: DataLoaderFactory): + self.inner = inner + + def prepare( + self, + *, + data_folder: str, + cached_data_folder: str, + dataset: MOABBDataset, + batch_size: int, + valid_ratio: float, + original_sample_rate: int, + target_subject_idx: int, + target_session_idx: Optional[int] = None, + sample_rate: Optional[int] = None, + fmin: Optional[float] = None, + fmax: Optional[float] = None, + tmin: Optional[float] = None, + tmax: Optional[float] = None, + n_steps_channel_selection: Optional[int] = None, + events_to_load: Optional[List[str]] = None, + save_prepared_dataset: bool = True, + ) -> Tuple[str, _SplitDataloaders]: + tail_path, datasets = self.inner.prepare( data_folder=data_folder, cached_data_folder=cached_data_folder, dataset=dataset, + batch_size=batch_size, + valid_ratio=valid_ratio, + original_sample_rate=original_sample_rate, + target_subject_idx=target_subject_idx, + target_session_idx=target_session_idx, + sample_rate=sample_rate, + fmin=fmin, + fmax=fmax, + tmin=tmin, + tmax=tmax, + n_steps_channel_selection=n_steps_channel_selection, events_to_load=events_to_load, + save_prepared_dataset=save_prepared_dataset, + ) + + edge_list = adjacency_mtx_to_edge_list(datasets["adjacency_mtx"]) + + for key in ("train", "valid", "test"): + datasets[key] = self.make_graph_dataloader( + datasets[key], + edge_list=edge_list, + ch_positions=torch.from_numpy(datasets["ch_positions"]), + ) + + return tail_path, datasets + + def make_graph_dataloader( + self, + dataloader: DataLoader, + edge_list: torch.Tensor, + ch_positions: torch.Tensor, + ) -> torch_geometric.loader.DataLoader: + dataset = _IterableDatasetFromGenerator( + # Transpose T, C -> C, T + torch_geometric.data.Data( + x=x.transpose(0, 1), + y=y, + edge_index=edge_list, + pos=ch_positions, + ) + for x, y in dataloader.dataset + ) + return torch_geometric.loader.DataLoader( + dataset, + batch_size=dataloader.batch_size, + pin_memory=dataloader.pin_memory, + shuffle=False, + ) + + +class LeaveOneSessionOut(BaseDataIOIterator): + """Leave one session out iterator for MOABB datasets. + Designing within-subject, cross-session and session-agnostic iterations on the dataset for a specific paradigm. + For each subject, one session is held back as test set and the remaining ones are used to train neural networks. + The validation set can be sampled from a separate (and held back) session if enough sessions are available; otherwise, the validation set is sampled from the training set. + All sets are extracted balanced across subjects, sessions and classes. + + Arguments + --------- + seed: int + Seed for random number generators. + """ + + def __init__(self, seed): + super().__init__("leave-one-session-out", seed) + + def get_data( + self, + target_subject_idx: int, + target_session_idx: Optional[int], + valid_ratio: float, + dataset: MOABBDataset, + data_folder: Optional[str], + cached_data_folder: Optional[str], + original_sample_rate: int, + sample_rate: Optional[int], + fmin: Optional[float], + fmax: Optional[float], + events_to_load: Optional[List[str]], + save_prepared_dataset: bool, + ) -> Tuple[dict, XYSplits]: + # preparing or loading dataset + data_dict = prepare_data( + idx_subject_to_prepare=target_subject_idx, + dataset=dataset, + data_folder=data_folder, + cached_data_folder=cached_data_folder, srate_in=original_sample_rate, srate_out=sample_rate, fmin=fmin, fmax=fmax, - idx_subject_to_prepare=target_subject_idx, save_prepared_dataset=save_prepared_dataset, + events_to_load=events_to_load, ) x = data_dict["x"] y = data_dict["y"] - srate = data_dict["srate"] - original_interval = data_dict["interval"] metadata = data_dict["metadata"] - if np.unique(metadata.session).shape[0] < 2: - raise ( - ValueError( - "The number of sessions in the dataset must be >= 2 for leave-one-session-out iterations" - ) - ) + + self._validate_metadata_or_raise(metadata) sessions = np.unique(metadata.session) - sess_id_test = [sessions[target_session_idx]] + + data_dict["session"] = sessions[target_session_idx] + + sess_id_test = [data_dict["session"]] sess_id_train = np.setdiff1d(sessions, sess_id_test) sess_id_train = list(sess_id_train) print( @@ -282,72 +583,21 @@ def prepare( x_test = x[idx_test, ...] y_test = y[idx_test] - # time cropping - if interval != original_interval: - x_train = crop_signals( - x=x_train, - srate=srate, - interval_in=original_interval, - interval_out=interval, - ) - x_valid = crop_signals( - x=x_valid, - srate=srate, - interval_in=original_interval, - interval_out=interval, - ) - x_test = crop_signals( - x=x_test, - srate=srate, - interval_in=original_interval, - interval_out=interval, - ) + return ( + data_dict, + XYSplits(x_train, y_train, x_valid, y_valid, x_test, y_test), + ) - # channel sampling - if n_steps_channel_selection is not None: - x_train = sample_channels( - x_train, - data_dict["adjacency_mtx"], - data_dict["channels"], - n_steps=n_steps_channel_selection, - ) - x_valid = sample_channels( - x_valid, - data_dict["adjacency_mtx"], - data_dict["channels"], - n_steps=n_steps_channel_selection, - ) - x_test = sample_channels( - x_test, - data_dict["adjacency_mtx"], - data_dict["channels"], - n_steps=n_steps_channel_selection, + def _validate_metadata_or_raise(self, metadata: pd.DataFrame): + if np.unique(metadata.session).shape[0] < 2: + raise ( + ValueError( + "The number of sessions in the dataset must be >= 2 for leave-one-session-out iterations" + ) ) - # swap axes: from (N_examples, C, T) to (N_examples, T, C) - x_train = np.swapaxes(x_train, -1, -2) - x_valid = np.swapaxes(x_valid, -1, -2) - x_test = np.swapaxes(x_test, -1, -2) - - # dataloaders - train_loader, valid_loader, test_loader = get_dataloader( - batch_size, (x_train, y_train), (x_valid, y_valid), (x_test, y_test) - ) - datasets = {} - datasets["train"] = train_loader - datasets["valid"] = valid_loader - datasets["test"] = test_loader - tail_path = os.path.join( - self.iterator_tag, - "sub-{0}".format( - str(dataset.subject_list[target_subject_idx]).zfill(3) - ), - sessions[target_session_idx], - ) - return tail_path, datasets - -class LeaveOneSubjectOut(object): +class LeaveOneSubjectOut(BaseDataIOIterator): """Leave one subject out iterator for MOABB datasets. Designing cross-subject, cross-session and subject-agnostic iterations on the dataset for a specific paradigm. One subject is held back as test set and the remaining ones are used to train neural networks. @@ -361,108 +611,40 @@ class LeaveOneSubjectOut(object): """ def __init__(self, seed): - self.iterator_tag = "leave-one-subject-out" - np.random.seed(seed) + super().__init__("leave-one-subject-out", seed) - def prepare( + def get_data( self, - data_folder=None, - cached_data_folder=None, - dataset=None, - batch_size=None, - valid_ratio=None, - target_subject_idx=None, - target_session_idx=None, - events_to_load=None, - original_sample_rate=None, - sample_rate=None, - fmin=None, - fmax=None, - tmin=None, - tmax=None, - save_prepared_dataset=None, - n_steps_channel_selection=None, + target_subject_idx: int, + target_session_idx: Optional[int], + valid_ratio: float, + dataset: MOABBDataset, + data_folder: Optional[str], + cached_data_folder: Optional[str], + original_sample_rate: int, + sample_rate: Optional[int], + fmin: Optional[float], + fmax: Optional[float], + events_to_load: Optional[List[str]], + save_prepared_dataset: bool, ): - """This function returns the pre-processed datasets (training, validation and test sets). - - Arguments - --------- - data_folder: str - String containing the path where data were downloaded. - cached_data_folder: str - String containing the path where data will be cached (into .pkl files) during preparation. - This is convenient to speed up overall training when performing multiple trainings on EEG signals - pre-processed always in the same way. - dataset: moabb.datasets.? - MOABB dataset. - batch_size: int - Mini-batch size. - valid_ratio: float - Ratio for extracting the validation set from the available training set (between 0 and 1). - target_subject_idx: int - Index of the subject to use to train the network. - target_session_idx: int - Index of the session to use to train the network. - For leave-one-subject-out this parameter is ininfluent (it is not used). - It was held for symmetry with leave-one-session-out iterator and for convenience with the rest of the code. - events_to_load: list - List of 'events' considered when loading the MOABB dataset. - It serves to load specific conditions (e.g., ["right_hand", "left_hand"] for specific movement conditions). - See MOABB documentation and reference publications of each dataset for additional details about datasets. - original_sample_rate: int - Sampling rate of the loaded dataset (Hz). - sample_rate: int - Target sampling rate (Hz). - fmin: float - Low cut-off frequency of the applied band-pass filtering (Hz). - fmax: float - High cut-off frequency of the applied band-pass filtering (Hz). - tmin: float - Start time of the EEG epoch, with respect to the event as defined in the dataset (s). - See MOABB documentation and reference publications of each dataset for additional details about datasets. - tmax: float - Stop time of the EEG epoch, with respect to the event as defined in the dataset (s). - See MOABB documentation and reference publications of each dataset for additional details about datasets. - save_prepared_dataset: bool - Flag to save the prepared dataset into a pkl file. - n_steps_channel_selection: int - Number of steps to perform when sampling a subset of channels from a seed channel, based on the adjacency matrix. - ... - - Returns - --------- - tail_path: str - String containing the relative path where results will be stored for the specified iterator, subject and session. - datasets: dict - Dictionary containing all sets (keys: 'train', 'test', 'valid'). - --------- - """ - interval = [tmin, tmax] - if len(dataset.subject_list) < 2: - raise ( - ValueError( - "The number of subjects in the dataset must be >= 2 for leave-one-subject-out iterations" - ) - ) - + self._validate_dataset_or_raise(dataset) # preparing or loading test set - data_dict = prepare_data( + target_data_dict = prepare_data( + idx_subject_to_prepare=target_subject_idx, + dataset=dataset, data_folder=data_folder, cached_data_folder=cached_data_folder, - dataset=dataset, - events_to_load=events_to_load, srate_in=original_sample_rate, srate_out=sample_rate, fmin=fmin, fmax=fmax, - idx_subject_to_prepare=target_subject_idx, save_prepared_dataset=save_prepared_dataset, + events_to_load=events_to_load, ) - x_test = data_dict["x"] - y_test = data_dict["y"] - original_interval = data_dict["interval"] - srate = data_dict["srate"] + x_test = target_data_dict["x"] + y_test = target_data_dict["y"] subject_idx_train = [ i @@ -488,16 +670,16 @@ def prepare( for subject_idx in subject_idx_train: # preparing or loading training/valid set data_dict = prepare_data( + idx_subject_to_prepare=subject_idx, + dataset=dataset, data_folder=data_folder, cached_data_folder=cached_data_folder, - dataset=dataset, - events_to_load=events_to_load, srate_in=original_sample_rate, srate_out=sample_rate, fmin=fmin, fmax=fmax, - idx_subject_to_prepare=subject_idx, save_prepared_dataset=save_prepared_dataset, + events_to_load=events_to_load, ) tmp_x_train = data_dict["x"] @@ -529,70 +711,21 @@ def prepare( y_train.extend(tmp_y_train) x_valid.extend(tmp_x_valid) y_valid.extend(tmp_y_valid) + x_train = np.array(x_train) y_train = np.array(y_train) x_valid = np.array(x_valid) y_valid = np.array(y_valid) - # time cropping - if interval != original_interval: - x_train = crop_signals( - x=x_train, - srate=srate, - interval_in=original_interval, - interval_out=interval, - ) - x_valid = crop_signals( - x=x_valid, - srate=srate, - interval_in=original_interval, - interval_out=interval, - ) - x_test = crop_signals( - x=x_test, - srate=srate, - interval_in=original_interval, - interval_out=interval, - ) + return ( + target_data_dict, + (x_train, y_train, x_valid, y_valid, x_test, y_test,), + ) - # channel sampling - if n_steps_channel_selection is not None: - x_train = sample_channels( - x_train, - data_dict["adjacency_mtx"], - data_dict["channels"], - n_steps=n_steps_channel_selection, - ) - x_valid = sample_channels( - x_valid, - data_dict["adjacency_mtx"], - data_dict["channels"], - n_steps=n_steps_channel_selection, - ) - x_test = sample_channels( - x_test, - data_dict["adjacency_mtx"], - data_dict["channels"], - n_steps=n_steps_channel_selection, + def _validate_dataset_or_raise(self, dataset: MOABBDataset): + if len(dataset.subject_list) < 2: + raise ( + ValueError( + "The number of subjects in the dataset must be >= 2 for leave-one-subject-out iterations" + ) ) - - # swap axes: from (N_examples, C, T) to (N_examples, T, C) - x_train = np.swapaxes(x_train, -1, -2) - x_valid = np.swapaxes(x_valid, -1, -2) - x_test = np.swapaxes(x_test, -1, -2) - - # dataloaders - train_loader, valid_loader, test_loader = get_dataloader( - batch_size, (x_train, y_train), (x_valid, y_valid), (x_test, y_test) - ) - datasets = {} - datasets["train"] = train_loader - datasets["valid"] = valid_loader - datasets["test"] = test_loader - tail_path = os.path.join( - self.iterator_tag, - "sub-{0}".format( - str(dataset.subject_list[target_subject_idx]).zfill(3) - ), - ) - return tail_path, datasets diff --git a/benchmarks/MOABB/utils/graph_iterators.py b/benchmarks/MOABB/utils/graph_iterators.py new file mode 100644 index 000000000..7ba29eb07 --- /dev/null +++ b/benchmarks/MOABB/utils/graph_iterators.py @@ -0,0 +1,336 @@ +""" +Graph Data Iterators for MOABB Datasets and Paradigms + +This module provides various data iterators tailored for MOABB datasets and paradigms which +require the electrode position and adjacency data. + +Different training strategies have been implemented as distinct data iterators, including: +- Leave-One-Session-Out +- Leave-One-Subject-Out + +Authors +------ +Drew Wagner, 2024 +""" + +import abc +import random +from typing import Iterable + +import numpy as np +import pandas as pd +import scipy +import torch +from mne import EpochsArray +from mne.channels import find_ch_adjacency +from moabb import paradigms +from torch import nn +from torch.nn import functional as F +from torch.utils.data import ChainDataset, IterableDataset +from torch_geometric.data import Batch, Data +from torch_geometric.loader import DataLoader +from mne.utils.config import set_config, get_config + + +def rename_subjects(dataset, subjects): + if isinstance(subjects, callable): + dataset.subject_list = subjects(dataset.subject_list) + elif isinstance(subjects, dict): + dataset.subject_list = [subjects[sub] for sub in dataset.subject_list] + elif isinstance(dataset, (list, tuple)): + dataset.subject_list = subjects + + return dataset + + +def _make_paradigm( + dataset, *, fmax, resample=128, fmin=0, tmin=0, tmax=None, **kwargs +): + if tmax is None: + tmax = dataset.interval[1] - dataset.interval[0] + + Paradigm = dict( + imagery=paradigms.MotorImagery, + p300=paradigms.P300, + ssvep=paradigms.SSVEP, + )[dataset.paradigm] + + if dataset.paradigm == 'p300': + kwargs.pop('events') + + return Paradigm( + resample=resample, fmin=fmin, fmax=fmax, tmin=tmin, tmax=tmax, **kwargs + ) + + +class PositionNoise(nn.Module): + def __init__(self, sigma=1.0): + super().__init__() + self.sigma = sigma + + def forward(self, x): + x.pos = x.pos + torch.randn_like(x.pos) * self.sigma + return x + + +class NodeDrop(nn.Module): + def __init__(self, choose_k): + super().__init__() + self.k = choose_k + + def forward(self, x): + del x.edge_index + x = x.to_data_list() + for d in x: + mask = torch.randperm(d.x.shape[0], device=d.x.device)[: self.k] + d.x = d.x[mask, :] + d.pos = d.pos[mask, :] + + return Batch.from_data_list(x) + + +class AsGeometricData(IterableDataset): + X: EpochsArray + Y: np.ndarray + metadata: pd.DataFrame + pos: np.ndarray + adjacency_mtx: np.ndarray + edge_list: np.ndarray + + def __init__( + self, + dataset, + target_subjects=None, + **paradigm_kwargs, + ): + self.dataset = dataset + self.paradigm = _make_paradigm(dataset, **paradigm_kwargs) + self.subjects = target_subjects + + self.is_loaded = False + + def load(self): + self.X, self.Y, self.metadata = self.paradigm.get_data( + self.dataset, + subjects=self.subjects, + return_epochs=True, + cache_config=dict( + save_epochs=True, + use=True), + ) + + self.pos = list( + self.X.info.get_montage().get_positions()["ch_pos"].values() + ) + self.pos = torch.tensor(self.pos).float().contiguous() + + adjacency_mtx, _ = find_ch_adjacency(self.X.info, ch_type="eeg") + self.adjacency_mtx = scipy.sparse.csr_matrix.toarray( + adjacency_mtx + ) # from sparse mtx to ndarray + self.edge_list = np.vstack(np.nonzero(adjacency_mtx)) + self.edge_list = torch.from_numpy(self.edge_list).float().contiguous() + + self.is_loaded = True + + def __iter__(self): + if not self.is_loaded: + self.load() + + for x, y, (_, m) in zip(self.X, self.Y, self.metadata.iterrows()): + yield Data( + x=torch.from_numpy(x).float(), + y=self.dataset.event_id[y] - 1, + edge_index=self.edge_list, + pos=self.pos, + **m, + ) + + +class BaseGraphData(abc.ABC): + train: DataLoader + valid: DataLoader + test: DataLoader + + def __init__( + self, + datasets, + target_subjects=None, + target_sessions=None, + valid_ratio=0.2, + **paradigm_kwargs, + ): + if not isinstance(datasets, Iterable): + datasets = [datasets] + + self.datasets = datasets + if not isinstance(target_subjects, (list, tuple)): + target_subjects = [target_subjects] + if not isinstance(target_sessions, (list, tuple)): + target_sessions = [target_sessions] + self.target_subjects = list(map(int, target_subjects)) + self.target_sessions = list(map(int, target_sessions)) + self.target_session_names = None + self.data = ChainDataset(self.make_geometric_data(paradigm_kwargs)) + self.valid_ratio = valid_ratio + + @abc.abstractmethod + def make_geometric_data(self, paradigm_kwargs): ... + + @abc.abstractmethod + def split_test_data(self, data): ... + + def data_statistics(self, data): + min_C = np.Inf + max_C = 0 + min_T = np.Inf + max_T = 0 + + for d in data: + C, T, *_ = d.x.shape + min_C = min(min_C, C) + max_C = max(max_C, C) + min_T = min(min_T, T) + max_T = max(max_T, T) + + return min_C, max_C, min_T, max_T + + def pad_data(self, data, max_T): + for d in data: + T = d.x.shape[1] + if T < max_T: + d.x = F.pad(d.x, (0, max_T - T)) + return data + + def prepare( + self, + data_folder=None, + cached_data_folder=None, + batch_size=1, + exclude_keys=None, + ): + if data_folder is not None: + mne_cfg = get_config() + for a in mne_cfg.keys(): + if ( + mne_cfg[a] != data_folder + ): # reducing writes on mne cfg file to avoid conflicts in parallel trainings + set_config(a, data_folder) + if cached_data_folder is None: + cached_data_folder = data_folder + + data = list(self.data) + min_C, max_C, min_T, max_T = self.data_statistics(data) + data = self.pad_data(data, max_T) + train_valid_data, test_data = self.split_test_data(data) + assert ( + len(train_valid_data) > 0 + ), "Invalid Split: No Training & Validation Data" + assert len(test_data) > 0, "Invalid Split: No Test Data" + + random.shuffle(train_valid_data) + train_valid_data = Batch.from_data_list(train_valid_data) + + valid_idxs = [] + for c in train_valid_data.y.unique(): + c_idxs = np.nonzero(train_valid_data.y == c) + idx = np.linspace( + 0, + c_idxs.shape[0] - 1, + round(self.valid_ratio * c_idxs.shape[0]), + ).astype(int) + valid_idxs.extend(c_idxs[idx]) + + valid_idxs = np.array(valid_idxs) + train_idxs = np.setdiff1d( + np.arange(len(train_valid_data.y)), valid_idxs + ) + + train = train_valid_data.index_select(train_idxs) + valid = train_valid_data.index_select(valid_idxs) + + if exclude_keys is None: + exclude_keys = [] + exclude_keys += ["subject", "session", "run"] + + self.train = DataLoader( + train, + batch_size=batch_size, + pin_memory=True, + shuffle=True, + exclude_keys=exclude_keys, + ) + self.valid = DataLoader( + valid, + batch_size=batch_size, + pin_memory=True, + exclude_keys=exclude_keys, + ) + self.test = DataLoader( + test_data, + batch_size=batch_size, + pin_memory=True, + exclude_keys=exclude_keys, + ) + + return { + "train": self.train, + "valid": self.valid, + "test": self.test, + "T": max_T, + "C": max_C, + "min_T": min_T, + "min_C": min_C, + } + + +class LeaveOneSessionOut(BaseGraphData): + def make_geometric_data(self, paradigm_kwargs): + return [ + AsGeometricData( + dataset, + # Pass target subject for IO efficiency + target_subjects=list( + set(self.target_subjects).intersection(dataset.subject_list) + ), + **paradigm_kwargs, + ) + for dataset in self.datasets + if set(self.target_subjects).intersection(dataset.subject_list) + ] + + def split_test_data(self, data): + other, test = [], [] + sessions = set() + target_sessions = set() + for d in data: + sessions.add(d.session) + sess_idx = sorted(sessions).index(d.session) + if sess_idx in self.target_sessions: + target_sessions.add(d.session) + test.append(d) + else: + other.append(d) + + self.target_session_names = target_sessions + return other, test + + +class LeaveOneSubjectOut(BaseGraphData): + def make_geometric_data(self, paradigm_kwargs): + return [ + AsGeometricData( + dataset, + **paradigm_kwargs, + ) + for dataset in self.datasets + ] + + def split_test_data(self, data): + other, test = [], [] + for d in data: + if d.subject in self.target_subjects: + test.append(d) + else: + other.append(d) + return other, test diff --git a/benchmarks/MOABB/utils/prepare.py b/benchmarks/MOABB/utils/prepare.py index 9f6371be4..b2bcbab68 100644 --- a/benchmarks/MOABB/utils/prepare.py +++ b/benchmarks/MOABB/utils/prepare.py @@ -78,9 +78,16 @@ def get_output_dict( resample=srate_out, # downsample ) - x, y, labels, metadata, channels, adjacency_mtx, srate = load_data( - paradigm, dataset, [subject] - ) + ( + x, + y, + labels, + metadata, + channels, + adjacency_mtx, + ch_positions, + srate, + ) = load_data(paradigm, dataset, [subject]) if verbose == 1: for l in np.unique(labels): @@ -106,6 +113,7 @@ def get_output_dict( output_dict["channels"] = channels output_dict["adjacency_mtx"] = adjacency_mtx + output_dict["ch_positions"] = ch_positions output_dict["x"] = x output_dict["y"] = y output_dict["labels"] = labels @@ -122,6 +130,7 @@ def load_data(paradigm, dataset, idx): In addition metadata, channel names and the sampling rate are provided too.""" x, labels, metadata = paradigm.get_data(dataset, idx, True) ch_names = x.info.ch_names + ch_positions = x.info.get_montage().get_positions()["ch_pos"] adjacency, _ = find_ch_adjacency(x.info, ch_type="eeg") adjacency_mtx = scipy.sparse.csr_matrix.toarray( adjacency @@ -132,7 +141,7 @@ def load_data(paradigm, dataset, idx): y = [dataset.event_id[yy] for yy in labels] y = np.array(y) y -= y.min() - return x, y, labels, metadata, ch_names, adjacency_mtx, srate + return x, y, labels, metadata, ch_names, adjacency_mtx, ch_positions, srate def download_data(data_folder, dataset): diff --git a/benchmarks/MOABB/visualize_spatial_focus.ipynb b/benchmarks/MOABB/visualize_spatial_focus.ipynb new file mode 100644 index 000000000..0153bc7da --- /dev/null +++ b/benchmarks/MOABB/visualize_spatial_focus.ipynb @@ -0,0 +1,5049 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "186f51ac-1412-4028-bbde-a75981f95b40", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/envs/pytorch/lib/python3.11/site-packages/torch/cuda/__init__.py:624: UserWarning: Can't initialize NVML\n", + " warnings.warn(\"Can't initialize NVML\")\n" + ] + } + ], + "source": [ + "from train import load_hparams_and_dataset_iterators\n", + "import torch\n", + "\n", + "import matplotlib.pyplot as plt\n", + "from mne.viz import plot_topomap\n", + "import mne.channels\n", + "import glob\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "9acb2196-6810-475f-8e46-4f6f989161b5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "moabb.utils - BNCI2014004 has been renamed to BNCI2014_004. BNCI2014004 will be removed in version 1.1.\n", + "moabb.datasets.base - The dataset class name 'BNCI2014004' must be an abbreviation of its code 'BNCI2014-004'. See moabb.datasets.base.is_abbrev for more information.\n", + "Prepare dataset iterators...\n", + "moabb.paradigms.motor_imagery - Choosing from all possible events\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-1 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-0train/epo/sub-1_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-1train/epo/sub-1_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-2train/epo/sub-1_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-3test/epo/sub-1_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-4test/epo/sub-1_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-1 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-2 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-0train/epo/sub-2_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-1train/epo/sub-2_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-2train/epo/sub-2_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-3test/epo/sub-2_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-4test/epo/sub-2_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-2 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-3 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-0train/epo/sub-3_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-1train/epo/sub-3_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-2train/epo/sub-3_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-3test/epo/sub-3_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-4test/epo/sub-3_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-3 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-4 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-0train/epo/sub-4_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-1train/epo/sub-4_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "140 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-2train/epo/sub-4_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-3test/epo/sub-4_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-4test/epo/sub-4_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-4 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-5 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-0train/epo/sub-5_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-1train/epo/sub-5_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "140 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-2train/epo/sub-5_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-3test/epo/sub-5_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-4test/epo/sub-5_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-5 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-6 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-0train/epo/sub-6_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-1train/epo/sub-6_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-2train/epo/sub-6_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-3test/epo/sub-6_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-4test/epo/sub-6_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-6 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-7 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-0train/epo/sub-7_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-1train/epo/sub-7_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-2train/epo/sub-7_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-3test/epo/sub-7_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-4test/epo/sub-7_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-7 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-8 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-0train/epo/sub-8_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-1train/epo/sub-8_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-2train/epo/sub-8_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-3test/epo/sub-8_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-4test/epo/sub-8_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-8 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-9 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-0train/epo/sub-9_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-1train/epo/sub-9_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-2train/epo/sub-9_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-3test/epo/sub-9_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-4test/epo/sub-9_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-9 datatype-epo desc-8545cc1\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 140 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/envs/pytorch/lib/python3.11/site-packages/moabb/paradigms/base.py:350: RuntimeWarning: Concatenation of Annotations within Epochs is not supported yet. All annotations will be dropped.\n", + " X = mne.concatenate_epochs(X)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 140 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Adding metadata with 3 columns\n", + "6520 matching events found\n", + "No baseline correction applied\n", + "Could not find a adjacency matrix for the data. Computing adjacency based on Delaunay triangulations.\n", + "-- number of adjacent vertices : 3\n", + "moabb.utils - BNCI2014004 has been renamed to BNCI2014_004. BNCI2014004 will be removed in version 1.1.\n", + "moabb.datasets.base - The dataset class name 'BNCI2014004' must be an abbreviation of its code 'BNCI2014-004'. See moabb.datasets.base.is_abbrev for more information.\n", + "speechbrain.core - Beginning experiment!\n", + "speechbrain.core - Experiment folder: /tmp/foo/leave-one-subject-out/sub-001\n", + "moabb.utils - BNCI2014004 has been renamed to BNCI2014_004. BNCI2014004 will be removed in version 1.1.\n", + "moabb.datasets.base - The dataset class name 'BNCI2014004' must be an abbreviation of its code 'BNCI2014-004'. See moabb.datasets.base.is_abbrev for more information.\n", + "Prepare dataset iterators...\n", + "moabb.paradigms.motor_imagery - Choosing from all possible events\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-1 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-0train/epo/sub-1_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-1train/epo/sub-1_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-2train/epo/sub-1_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-3test/epo/sub-1_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-4test/epo/sub-1_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-1 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-2 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-0train/epo/sub-2_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-1train/epo/sub-2_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-2train/epo/sub-2_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-3test/epo/sub-2_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-4test/epo/sub-2_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-2 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-3 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-0train/epo/sub-3_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-1train/epo/sub-3_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-2train/epo/sub-3_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-3test/epo/sub-3_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-4test/epo/sub-3_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-3 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-4 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-0train/epo/sub-4_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-1train/epo/sub-4_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "140 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-2train/epo/sub-4_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-3test/epo/sub-4_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-4test/epo/sub-4_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-4 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-5 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-0train/epo/sub-5_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-1train/epo/sub-5_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "140 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-2train/epo/sub-5_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-3test/epo/sub-5_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-4test/epo/sub-5_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-5 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-6 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-0train/epo/sub-6_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-1train/epo/sub-6_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-2train/epo/sub-6_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-3test/epo/sub-6_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-4test/epo/sub-6_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-6 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-7 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-0train/epo/sub-7_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-1train/epo/sub-7_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-2train/epo/sub-7_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-3test/epo/sub-7_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-4test/epo/sub-7_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-7 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-8 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-0train/epo/sub-8_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-1train/epo/sub-8_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-2train/epo/sub-8_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-3test/epo/sub-8_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-4test/epo/sub-8_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-8 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-9 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-0train/epo/sub-9_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-1train/epo/sub-9_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-2train/epo/sub-9_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-3test/epo/sub-9_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-4test/epo/sub-9_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-9 datatype-epo desc-8545cc1\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 140 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 140 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/envs/pytorch/lib/python3.11/site-packages/moabb/paradigms/base.py:350: RuntimeWarning: Concatenation of Annotations within Epochs is not supported yet. All annotations will be dropped.\n", + " X = mne.concatenate_epochs(X)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Adding metadata with 3 columns\n", + "6520 matching events found\n", + "No baseline correction applied\n", + "Could not find a adjacency matrix for the data. Computing adjacency based on Delaunay triangulations.\n", + "-- number of adjacent vertices : 3\n", + "moabb.utils - BNCI2014004 has been renamed to BNCI2014_004. BNCI2014004 will be removed in version 1.1.\n", + "moabb.datasets.base - The dataset class name 'BNCI2014004' must be an abbreviation of its code 'BNCI2014-004'. See moabb.datasets.base.is_abbrev for more information.\n", + "speechbrain.core - Beginning experiment!\n", + "speechbrain.core - Experiment folder: /tmp/foo/leave-one-subject-out/sub-002\n", + "moabb.utils - BNCI2014004 has been renamed to BNCI2014_004. BNCI2014004 will be removed in version 1.1.\n", + "moabb.datasets.base - The dataset class name 'BNCI2014004' must be an abbreviation of its code 'BNCI2014-004'. See moabb.datasets.base.is_abbrev for more information.\n", + "Prepare dataset iterators...\n", + "moabb.paradigms.motor_imagery - Choosing from all possible events\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-1 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-0train/epo/sub-1_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-1train/epo/sub-1_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-2train/epo/sub-1_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-3test/epo/sub-1_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-4test/epo/sub-1_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-1 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-2 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-0train/epo/sub-2_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-1train/epo/sub-2_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-2train/epo/sub-2_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-3test/epo/sub-2_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-4test/epo/sub-2_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-2 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-3 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-0train/epo/sub-3_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-1train/epo/sub-3_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-2train/epo/sub-3_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-3test/epo/sub-3_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-4test/epo/sub-3_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-3 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-4 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-0train/epo/sub-4_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-1train/epo/sub-4_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "140 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-2train/epo/sub-4_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-3test/epo/sub-4_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-4test/epo/sub-4_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-4 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-5 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-0train/epo/sub-5_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-1train/epo/sub-5_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "140 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-2train/epo/sub-5_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-3test/epo/sub-5_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-4test/epo/sub-5_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-5 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-6 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-0train/epo/sub-6_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-1train/epo/sub-6_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-2train/epo/sub-6_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-3test/epo/sub-6_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-4test/epo/sub-6_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-6 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-7 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-0train/epo/sub-7_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-1train/epo/sub-7_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-2train/epo/sub-7_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-3test/epo/sub-7_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-4test/epo/sub-7_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-7 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-8 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-0train/epo/sub-8_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-1train/epo/sub-8_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-2train/epo/sub-8_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-3test/epo/sub-8_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-4test/epo/sub-8_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-8 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-9 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-0train/epo/sub-9_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-1train/epo/sub-9_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-2train/epo/sub-9_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-3test/epo/sub-9_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-4test/epo/sub-9_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-9 datatype-epo desc-8545cc1\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 140 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 140 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/envs/pytorch/lib/python3.11/site-packages/moabb/paradigms/base.py:350: RuntimeWarning: Concatenation of Annotations within Epochs is not supported yet. All annotations will be dropped.\n", + " X = mne.concatenate_epochs(X)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Adding metadata with 3 columns\n", + "6520 matching events found\n", + "No baseline correction applied\n", + "Could not find a adjacency matrix for the data. Computing adjacency based on Delaunay triangulations.\n", + "-- number of adjacent vertices : 3\n", + "moabb.utils - BNCI2014004 has been renamed to BNCI2014_004. BNCI2014004 will be removed in version 1.1.\n", + "moabb.datasets.base - The dataset class name 'BNCI2014004' must be an abbreviation of its code 'BNCI2014-004'. See moabb.datasets.base.is_abbrev for more information.\n", + "speechbrain.core - Beginning experiment!\n", + "speechbrain.core - Experiment folder: /tmp/foo/leave-one-subject-out/sub-003\n", + "moabb.utils - BNCI2014004 has been renamed to BNCI2014_004. BNCI2014004 will be removed in version 1.1.\n", + "moabb.datasets.base - The dataset class name 'BNCI2014004' must be an abbreviation of its code 'BNCI2014-004'. See moabb.datasets.base.is_abbrev for more information.\n", + "Prepare dataset iterators...\n", + "moabb.paradigms.motor_imagery - Choosing from all possible events\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-1 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-0train/epo/sub-1_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-1train/epo/sub-1_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-2train/epo/sub-1_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-3test/epo/sub-1_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-4test/epo/sub-1_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-1 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-2 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-0train/epo/sub-2_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-1train/epo/sub-2_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-2train/epo/sub-2_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-3test/epo/sub-2_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-4test/epo/sub-2_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-2 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-3 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-0train/epo/sub-3_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-1train/epo/sub-3_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-2train/epo/sub-3_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-3test/epo/sub-3_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-4test/epo/sub-3_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-3 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-4 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-0train/epo/sub-4_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-1train/epo/sub-4_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "140 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-2train/epo/sub-4_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-3test/epo/sub-4_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-4test/epo/sub-4_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-4 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-5 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-0train/epo/sub-5_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-1train/epo/sub-5_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "140 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-2train/epo/sub-5_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-3test/epo/sub-5_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-4test/epo/sub-5_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-5 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-6 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-0train/epo/sub-6_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-1train/epo/sub-6_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-2train/epo/sub-6_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-3test/epo/sub-6_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-4test/epo/sub-6_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-6 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-7 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-0train/epo/sub-7_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-1train/epo/sub-7_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-2train/epo/sub-7_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-3test/epo/sub-7_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-4test/epo/sub-7_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-7 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-8 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-0train/epo/sub-8_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-1train/epo/sub-8_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-2train/epo/sub-8_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-3test/epo/sub-8_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-4test/epo/sub-8_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-8 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-9 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-0train/epo/sub-9_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-1train/epo/sub-9_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-2train/epo/sub-9_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-3test/epo/sub-9_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-4test/epo/sub-9_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-9 datatype-epo desc-8545cc1\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 140 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/envs/pytorch/lib/python3.11/site-packages/moabb/paradigms/base.py:350: RuntimeWarning: Concatenation of Annotations within Epochs is not supported yet. All annotations will be dropped.\n", + " X = mne.concatenate_epochs(X)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 140 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Adding metadata with 3 columns\n", + "6520 matching events found\n", + "No baseline correction applied\n", + "Could not find a adjacency matrix for the data. Computing adjacency based on Delaunay triangulations.\n", + "-- number of adjacent vertices : 3\n", + "moabb.utils - BNCI2014004 has been renamed to BNCI2014_004. BNCI2014004 will be removed in version 1.1.\n", + "moabb.datasets.base - The dataset class name 'BNCI2014004' must be an abbreviation of its code 'BNCI2014-004'. See moabb.datasets.base.is_abbrev for more information.\n", + "speechbrain.core - Beginning experiment!\n", + "speechbrain.core - Experiment folder: /tmp/foo/leave-one-subject-out/sub-004\n", + "moabb.utils - BNCI2014004 has been renamed to BNCI2014_004. BNCI2014004 will be removed in version 1.1.\n", + "moabb.datasets.base - The dataset class name 'BNCI2014004' must be an abbreviation of its code 'BNCI2014-004'. See moabb.datasets.base.is_abbrev for more information.\n", + "Prepare dataset iterators...\n", + "moabb.paradigms.motor_imagery - Choosing from all possible events\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-1 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-0train/epo/sub-1_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-1train/epo/sub-1_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-2train/epo/sub-1_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-3test/epo/sub-1_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-4test/epo/sub-1_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-1 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-2 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-0train/epo/sub-2_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-1train/epo/sub-2_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-2train/epo/sub-2_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-3test/epo/sub-2_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-4test/epo/sub-2_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-2 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-3 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-0train/epo/sub-3_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-1train/epo/sub-3_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-2train/epo/sub-3_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-3test/epo/sub-3_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-4test/epo/sub-3_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-3 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-4 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-0train/epo/sub-4_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-1train/epo/sub-4_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "140 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-2train/epo/sub-4_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-3test/epo/sub-4_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-4test/epo/sub-4_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-4 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-5 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-0train/epo/sub-5_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-1train/epo/sub-5_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "140 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-2train/epo/sub-5_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-3test/epo/sub-5_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-4test/epo/sub-5_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-5 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-6 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-0train/epo/sub-6_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-1train/epo/sub-6_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-2train/epo/sub-6_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-3test/epo/sub-6_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-4test/epo/sub-6_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-6 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-7 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-0train/epo/sub-7_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-1train/epo/sub-7_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-2train/epo/sub-7_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-3test/epo/sub-7_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-4test/epo/sub-7_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-7 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-8 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-0train/epo/sub-8_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-1train/epo/sub-8_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-2train/epo/sub-8_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-3test/epo/sub-8_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-4test/epo/sub-8_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-8 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-9 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-0train/epo/sub-9_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-1train/epo/sub-9_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-2train/epo/sub-9_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-3test/epo/sub-9_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-4test/epo/sub-9_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-9 datatype-epo desc-8545cc1\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 140 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/envs/pytorch/lib/python3.11/site-packages/moabb/paradigms/base.py:350: RuntimeWarning: Concatenation of Annotations within Epochs is not supported yet. All annotations will be dropped.\n", + " X = mne.concatenate_epochs(X)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading data for 140 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Adding metadata with 3 columns\n", + "6520 matching events found\n", + "No baseline correction applied\n", + "Could not find a adjacency matrix for the data. Computing adjacency based on Delaunay triangulations.\n", + "-- number of adjacent vertices : 3\n", + "moabb.utils - BNCI2014004 has been renamed to BNCI2014_004. BNCI2014004 will be removed in version 1.1.\n", + "moabb.datasets.base - The dataset class name 'BNCI2014004' must be an abbreviation of its code 'BNCI2014-004'. See moabb.datasets.base.is_abbrev for more information.\n", + "speechbrain.core - Beginning experiment!\n", + "speechbrain.core - Experiment folder: /tmp/foo/leave-one-subject-out/sub-005\n", + "moabb.utils - BNCI2014004 has been renamed to BNCI2014_004. BNCI2014004 will be removed in version 1.1.\n", + "moabb.datasets.base - The dataset class name 'BNCI2014004' must be an abbreviation of its code 'BNCI2014-004'. See moabb.datasets.base.is_abbrev for more information.\n", + "Prepare dataset iterators...\n", + "moabb.paradigms.motor_imagery - Choosing from all possible events\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-1 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-0train/epo/sub-1_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-1train/epo/sub-1_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-2train/epo/sub-1_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-3test/epo/sub-1_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-4test/epo/sub-1_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-1 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-2 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-0train/epo/sub-2_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-1train/epo/sub-2_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-2train/epo/sub-2_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-3test/epo/sub-2_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-4test/epo/sub-2_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-2 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-3 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-0train/epo/sub-3_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-1train/epo/sub-3_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-2train/epo/sub-3_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-3test/epo/sub-3_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-4test/epo/sub-3_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-3 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-4 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-0train/epo/sub-4_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-1train/epo/sub-4_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "140 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-2train/epo/sub-4_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-3test/epo/sub-4_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-4test/epo/sub-4_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-4 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-5 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-0train/epo/sub-5_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-1train/epo/sub-5_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "140 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-2train/epo/sub-5_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-3test/epo/sub-5_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-4test/epo/sub-5_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-5 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-6 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-0train/epo/sub-6_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-1train/epo/sub-6_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-2train/epo/sub-6_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-3test/epo/sub-6_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-4test/epo/sub-6_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-6 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-7 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-0train/epo/sub-7_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-1train/epo/sub-7_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-2train/epo/sub-7_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-3test/epo/sub-7_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-4test/epo/sub-7_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-7 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-8 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-0train/epo/sub-8_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-1train/epo/sub-8_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-2train/epo/sub-8_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-3test/epo/sub-8_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-4test/epo/sub-8_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-8 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-9 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-0train/epo/sub-9_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-1train/epo/sub-9_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-2train/epo/sub-9_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-3test/epo/sub-9_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-4test/epo/sub-9_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-9 datatype-epo desc-8545cc1\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 140 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/envs/pytorch/lib/python3.11/site-packages/moabb/paradigms/base.py:350: RuntimeWarning: Concatenation of Annotations within Epochs is not supported yet. All annotations will be dropped.\n", + " X = mne.concatenate_epochs(X)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading data for 140 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Adding metadata with 3 columns\n", + "6520 matching events found\n", + "No baseline correction applied\n", + "Could not find a adjacency matrix for the data. Computing adjacency based on Delaunay triangulations.\n", + "-- number of adjacent vertices : 3\n", + "moabb.utils - BNCI2014004 has been renamed to BNCI2014_004. BNCI2014004 will be removed in version 1.1.\n", + "moabb.datasets.base - The dataset class name 'BNCI2014004' must be an abbreviation of its code 'BNCI2014-004'. See moabb.datasets.base.is_abbrev for more information.\n", + "speechbrain.core - Beginning experiment!\n", + "speechbrain.core - Experiment folder: /tmp/foo/leave-one-subject-out/sub-006\n", + "moabb.utils - BNCI2014004 has been renamed to BNCI2014_004. BNCI2014004 will be removed in version 1.1.\n", + "moabb.datasets.base - The dataset class name 'BNCI2014004' must be an abbreviation of its code 'BNCI2014-004'. See moabb.datasets.base.is_abbrev for more information.\n", + "Prepare dataset iterators...\n", + "moabb.paradigms.motor_imagery - Choosing from all possible events\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-1 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-0train/epo/sub-1_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-1train/epo/sub-1_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-2train/epo/sub-1_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-3test/epo/sub-1_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-4test/epo/sub-1_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-1 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-2 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-0train/epo/sub-2_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-1train/epo/sub-2_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-2train/epo/sub-2_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-3test/epo/sub-2_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-4test/epo/sub-2_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-2 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-3 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-0train/epo/sub-3_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-1train/epo/sub-3_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-2train/epo/sub-3_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-3test/epo/sub-3_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-4test/epo/sub-3_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-3 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-4 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-0train/epo/sub-4_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-1train/epo/sub-4_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "140 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-2train/epo/sub-4_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-3test/epo/sub-4_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-4test/epo/sub-4_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-4 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-5 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-0train/epo/sub-5_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-1train/epo/sub-5_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "140 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-2train/epo/sub-5_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-3test/epo/sub-5_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-4test/epo/sub-5_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-5 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-6 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-0train/epo/sub-6_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-1train/epo/sub-6_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-2train/epo/sub-6_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-3test/epo/sub-6_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-4test/epo/sub-6_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-6 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-7 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-0train/epo/sub-7_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-1train/epo/sub-7_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-2train/epo/sub-7_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-3test/epo/sub-7_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-4test/epo/sub-7_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-7 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-8 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-0train/epo/sub-8_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-1train/epo/sub-8_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-2train/epo/sub-8_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-3test/epo/sub-8_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-4test/epo/sub-8_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-8 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-9 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-0train/epo/sub-9_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-1train/epo/sub-9_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-2train/epo/sub-9_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-3test/epo/sub-9_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-4test/epo/sub-9_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-9 datatype-epo desc-8545cc1\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 140 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 140 events and 488 original time points ...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/envs/pytorch/lib/python3.11/site-packages/moabb/paradigms/base.py:350: RuntimeWarning: Concatenation of Annotations within Epochs is not supported yet. All annotations will be dropped.\n", + " X = mne.concatenate_epochs(X)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Adding metadata with 3 columns\n", + "6520 matching events found\n", + "No baseline correction applied\n", + "Could not find a adjacency matrix for the data. Computing adjacency based on Delaunay triangulations.\n", + "-- number of adjacent vertices : 3\n", + "moabb.utils - BNCI2014004 has been renamed to BNCI2014_004. BNCI2014004 will be removed in version 1.1.\n", + "moabb.datasets.base - The dataset class name 'BNCI2014004' must be an abbreviation of its code 'BNCI2014-004'. See moabb.datasets.base.is_abbrev for more information.\n", + "speechbrain.core - Beginning experiment!\n", + "speechbrain.core - Experiment folder: /tmp/foo/leave-one-subject-out/sub-007\n", + "moabb.utils - BNCI2014004 has been renamed to BNCI2014_004. BNCI2014004 will be removed in version 1.1.\n", + "moabb.datasets.base - The dataset class name 'BNCI2014004' must be an abbreviation of its code 'BNCI2014-004'. See moabb.datasets.base.is_abbrev for more information.\n", + "Prepare dataset iterators...\n", + "moabb.paradigms.motor_imagery - Choosing from all possible events\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-1 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-0train/epo/sub-1_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-1train/epo/sub-1_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-2train/epo/sub-1_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-3test/epo/sub-1_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-4test/epo/sub-1_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-1 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-2 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-0train/epo/sub-2_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-1train/epo/sub-2_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-2train/epo/sub-2_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-3test/epo/sub-2_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-4test/epo/sub-2_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-2 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-3 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-0train/epo/sub-3_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-1train/epo/sub-3_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-2train/epo/sub-3_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-3test/epo/sub-3_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-4test/epo/sub-3_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-3 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-4 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-0train/epo/sub-4_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-1train/epo/sub-4_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "140 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-2train/epo/sub-4_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-3test/epo/sub-4_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-4test/epo/sub-4_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-4 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-5 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-0train/epo/sub-5_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-1train/epo/sub-5_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "140 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-2train/epo/sub-5_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-3test/epo/sub-5_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-4test/epo/sub-5_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-5 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-6 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-0train/epo/sub-6_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-1train/epo/sub-6_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-2train/epo/sub-6_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-3test/epo/sub-6_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-4test/epo/sub-6_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-6 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-7 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-0train/epo/sub-7_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-1train/epo/sub-7_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-2train/epo/sub-7_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-3test/epo/sub-7_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-4test/epo/sub-7_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-7 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-8 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-0train/epo/sub-8_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-1train/epo/sub-8_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-2train/epo/sub-8_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-3test/epo/sub-8_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-4test/epo/sub-8_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-8 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-9 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-0train/epo/sub-9_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-1train/epo/sub-9_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-2train/epo/sub-9_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-3test/epo/sub-9_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-4test/epo/sub-9_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-9 datatype-epo desc-8545cc1\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 140 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 140 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/envs/pytorch/lib/python3.11/site-packages/moabb/paradigms/base.py:350: RuntimeWarning: Concatenation of Annotations within Epochs is not supported yet. All annotations will be dropped.\n", + " X = mne.concatenate_epochs(X)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Adding metadata with 3 columns\n", + "6520 matching events found\n", + "No baseline correction applied\n", + "Could not find a adjacency matrix for the data. Computing adjacency based on Delaunay triangulations.\n", + "-- number of adjacent vertices : 3\n", + "moabb.utils - BNCI2014004 has been renamed to BNCI2014_004. BNCI2014004 will be removed in version 1.1.\n", + "moabb.datasets.base - The dataset class name 'BNCI2014004' must be an abbreviation of its code 'BNCI2014-004'. See moabb.datasets.base.is_abbrev for more information.\n", + "speechbrain.core - Beginning experiment!\n", + "speechbrain.core - Experiment folder: /tmp/foo/leave-one-subject-out/sub-008\n", + "moabb.utils - BNCI2014004 has been renamed to BNCI2014_004. BNCI2014004 will be removed in version 1.1.\n", + "moabb.datasets.base - The dataset class name 'BNCI2014004' must be an abbreviation of its code 'BNCI2014-004'. See moabb.datasets.base.is_abbrev for more information.\n", + "Prepare dataset iterators...\n", + "moabb.paradigms.motor_imagery - Choosing from all possible events\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-1 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-0train/epo/sub-1_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-1train/epo/sub-1_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-2train/epo/sub-1_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-3test/epo/sub-1_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-4test/epo/sub-1_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-1 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-2 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-0train/epo/sub-2_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-1train/epo/sub-2_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-2train/epo/sub-2_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-3test/epo/sub-2_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-4test/epo/sub-2_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-2 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-3 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-0train/epo/sub-3_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-1train/epo/sub-3_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-2train/epo/sub-3_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-3test/epo/sub-3_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-4test/epo/sub-3_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-3 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-4 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-0train/epo/sub-4_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-1train/epo/sub-4_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "140 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-2train/epo/sub-4_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-3test/epo/sub-4_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-4test/epo/sub-4_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-4 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-5 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-0train/epo/sub-5_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-1train/epo/sub-5_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "140 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-2train/epo/sub-5_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-3test/epo/sub-5_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-4test/epo/sub-5_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-5 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-6 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-0train/epo/sub-6_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-1train/epo/sub-6_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-2train/epo/sub-6_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-3test/epo/sub-6_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-4test/epo/sub-6_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-6 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-7 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-0train/epo/sub-7_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-1train/epo/sub-7_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-2train/epo/sub-7_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-3test/epo/sub-7_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-4test/epo/sub-7_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-7 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-8 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-0train/epo/sub-8_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-1train/epo/sub-8_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-2train/epo/sub-8_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-3test/epo/sub-8_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-4test/epo/sub-8_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-8 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-9 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-0train/epo/sub-9_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-1train/epo/sub-9_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-2train/epo/sub-9_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-3test/epo/sub-9_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-4test/epo/sub-9_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-9 datatype-epo desc-8545cc1\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/envs/pytorch/lib/python3.11/site-packages/moabb/paradigms/base.py:350: RuntimeWarning: Concatenation of Annotations within Epochs is not supported yet. All annotations will be dropped.\n", + " X = mne.concatenate_epochs(X)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 140 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 140 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Adding metadata with 3 columns\n", + "6520 matching events found\n", + "No baseline correction applied\n", + "Could not find a adjacency matrix for the data. Computing adjacency based on Delaunay triangulations.\n", + "-- number of adjacent vertices : 3\n", + "moabb.utils - BNCI2014004 has been renamed to BNCI2014_004. BNCI2014004 will be removed in version 1.1.\n", + "moabb.datasets.base - The dataset class name 'BNCI2014004' must be an abbreviation of its code 'BNCI2014-004'. See moabb.datasets.base.is_abbrev for more information.\n", + "speechbrain.core - Beginning experiment!\n", + "speechbrain.core - Experiment folder: /tmp/foo/leave-one-subject-out/sub-009\n" + ] + } + ], + "source": [ + "subject, session = 0, 0\n", + "weights_array = []\n", + "for subject in range(9):\n", + " hparams, ds = load_hparams_and_dataset_iterators('results/MotorImagery/BNCI2014004/SpatialEEGNet_cross/hopt/best_hparams.yaml', {}, {\n", + " \"data_folder\": \"/home/ubuntu/mne_data\",\n", + " \"output_folder\": \"/tmp/foo\",\n", + " \"cached_data_folder\": \"/home/ubuntu/mne_data/pkl\",\n", + " \"target_subject_idx\": subject,\n", + " \"target_session_idx\": 1,\n", + " \"data_iterator_name\": \"leave-one-subject-out\"\n", + " })\n", + " \n", + " sample = next(iter(ds['train'].dataset))\n", + " \n", + " ckpts = glob.glob(f'results/MotorImagery/BNCI2014004/SpatialEEGNet_cross/hopt/best/BJUZME/*/*/leave-one-subject-out/sub-{str(subject+1).zfill(3)}/save/*/model.ckpt')\n", + " for ckpt in ckpts:\n", + " hparams['model'].load_state_dict(torch.load(ckpt, map_location=torch.device('cpu')))\n", + " ch_positions = sample.pos\n", + " ch_positions_orig = sample.pos\n", + " ch_positions = (\n", + " 2 * ((ch_positions - ch_positions.min(0).values) / (ch_positions.max(0).values - ch_positions.min(0).values) - 0.5)\n", + " )\n", + " weights = hparams['model'].spatial_focus.similarity_module(ch_positions.float()).div_(hparams[\"spatial_focus_tau\"]).softmax(-2)\n", + " weights_array.append(weights)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "23631dc5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.66558677, 0.10161189, 0.23280133],\n", + " [0.5768434 , 0.13060276, 0.29255384],\n", + " [0.13782266, 0.3327731 , 0.5294043 ],\n", + " ...,\n", + " [0.7155922 , 0.08293536, 0.20147242],\n", + " [0.720004 , 0.16219407, 0.11780193],\n", + " [0.48455855, 0.2916509 , 0.22379057]], dtype=float32)" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "weights = torch.hstack(weights_array).T.cpu().detach().numpy()\n", + "weights" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "63bb42c0", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.cluster import KMeans" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "d0f47e25", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
KMeans(n_clusters=42)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "KMeans(n_clusters=42)" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "km = KMeans(n_clusters=hparams['projection_dim'])\n", + "km.fit(np.log(weights))" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "88581334-8c98-4078-a992-7cf19d7f47cf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.039826610100963125\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rows = int(np.ceil(hparams['projection_dim'] / 6))\n", + "fig, axes = plt.subplots(rows, 6, figsize=(2*6, 2*rows), layout=\"constrained\")\n", + "for ax in axes.flat:\n", + " ax.axis('off')\n", + "for i, ax in zip(range(km.cluster_centers_.shape[0]), axes.flat):\n", + " plot_topomap(data=np.exp(km.cluster_centers_[i]), pos=ch_positions_orig[:, [0, 1]], axes=ax, show=False)\n", + "\n", + "plt.savefig(\"bnci2014_004_clusters.pdf\", format=\"PDF\")\n", + "print(km.inertia_ / len(weights))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}