API Reference

This is the reference for classes (CamelCase names) and functions (underscore_case names) of Braindecode.

braindecode:

Classifier

braindecode.classifier:

EEGClassifier(module, *args[, criterion, ...])

Classifier that does not assume softmax activation.

Regressor

braindecode.regressor:

EEGRegressor(module, *args[, cropped, ...])

Regressor that calls loss function directly.

Models

braindecode.models.base:

EEGModuleMixin([n_outputs, n_chans, ...])

Mixin class for all EEG models in braindecode.

braindecode.models:

ShallowFBCSPNet([n_chans, n_outputs, ...])	Shallow ConvNet model from Schirrmeister et al 2017.
Deep4Net([n_chans, n_outputs, n_times, ...])	Deep ConvNet model from Schirrmeister et al 2017.
DeepSleepNet([n_outputs, return_feats, ...])	Sleep staging architecture from Supratak et al 2017.
EEGConformer([n_outputs, n_chans, ...])	EEG Conformer.
EEGInception([n_chans, n_outputs, n_times, ...])	EEG Inception for ERP-based classification
EEGInceptionERP([n_chans, n_outputs, ...])	EEG Inception for ERP-based classification
EEGInceptionMI([n_chans, n_outputs, ...])	EEG Inception for Motor Imagery, as proposed in [Rc36ed781f4f5-1]
ATCNet([n_chans, n_outputs, ...])	ATCNet model from [R2ecdb73d6ab9-1]
EEGITNet([n_outputs, n_chans, n_times, ...])	EEG-ITNet: An Explainable Inception Temporal
EEGNetv1([n_chans, n_outputs, n_times, ...])	EEGNet model from Lawhern et al. 2016.
EEGNetv4([n_chans, n_outputs, n_times, ...])	EEGNet v4 model from Lawhern et al 2018.
HybridNet([n_chans, n_outputs, n_times, ...])	Hybrid ConvNet model from Schirrmeister et al 2017.
EEGResNet([n_chans, n_outputs, n_times, ...])	Residual Network for EEG.
TCN([n_chans, n_outputs, n_blocks, ...])	Temporal Convolutional Network (TCN) from Bai et al 2018.
SleepStagerChambon2018([n_chans, sfreq, ...])	Sleep staging architecture from Chambon et al 2018.
SleepStagerBlanco2020([n_chans, sfreq, ...])	Sleep staging architecture from Blanco et al 2020.
SleepStagerEldele2021([sfreq, n_tce, ...])	Sleep Staging Architecture from Eldele et al 2021.
USleep([n_chans, sfreq, depth, ...])	Sleep staging architecture from Perslev et al 2021.
TIDNet([n_chans, n_outputs, n_times, ...])	Thinker Invariance DenseNet model from Kostas et al 2020.
get_output_shape(model, in_chans, ...)	Returns shape of neural network output for batch size equal 1.
TimeDistributed(module)	Apply module on multiple windows.

Training

braindecode.training:

CroppedLoss(loss_function)	Compute Loss after averaging predictions across time.
TimeSeriesLoss(loss_function)	Compute Loss between timeseries targets and predictions.
CroppedTrialEpochScoring(scoring[, ...])	Class to compute scores for trials from a model that predicts (super)crops.
CroppedTimeSeriesEpochScoring(scoring[, ...])	Class to compute scores for trials from a model that predicts (super)crops with time series target.
PostEpochTrainScoring(scoring[, ...])	Epoch Scoring class that recomputes predictions after the epoch on the training in validation mode.
mixup_criterion(preds, target)	Implements loss for Mixup for EEG data.
trial_preds_from_window_preds(preds, ...)	Assigning window predictions to trials while removing duplicate predictions.
predict_trials(module, dataset[, ...])	Create trialwise predictions and optionally also return trialwise labels from cropped dataset given module.

Datasets

braindecode.datasets:

BaseDataset(raw[, description, target_name, ...])	Returns samples from an mne.io.Raw object along with a target.
BaseConcatDataset(list_of_ds[, target_transform])	A base class for concatenated datasets.
WindowsDataset(windows[, description, ...])	Returns windows from an mne.Epochs object along with a target.
MOABBDataset(dataset_name, subject_ids[, ...])	A class for moabb datasets.
HGD(subject_ids)	High-gamma dataset described in Schirrmeister et al. 2017.
BNCI2014001(subject_ids)	BNCI 2014-001 Motor Imagery dataset.
TUH(path[, recording_ids, target_name, ...])	Temple University Hospital (TUH) EEG Corpus (www.isip.piconepress.com/projects/tuh_eeg/html/downloads.shtml#c_tueg).
TUHAbnormal(path[, recording_ids, ...])	Temple University Hospital (TUH) Abnormal EEG Corpus.
SleepPhysionet([subject_ids, recording_ids, ...])	Sleep Physionet dataset.
BCICompetitionIVDataset4([subject_ids])	BCI competition IV dataset 4.
create_from_X_y(X, y, drop_last_window, sfreq)	Create a BaseConcatDataset of WindowsDatasets from X and y to be used for decoding with skorch and braindecode, where X is a list of pre-cut trials and y are corresponding targets.
create_from_mne_raw(raws, ...[, ...])	Create WindowsDatasets from mne.RawArrays
create_from_mne_epochs(list_of_epochs, ...)	Create WindowsDatasets from mne.Epochs

Preprocessing

braindecode.preprocessing:

create_windows_from_events(concat_ds[, ...])	Create windows based on events in mne.Raw.
create_fixed_length_windows(concat_ds[, ...])	Windower that creates sliding windows.
create_windows_from_target_channels(concat_ds)
exponential_moving_demean(data[, ...])	Perform exponential moving demeanining.
exponential_moving_standardize(data[, ...])	Perform exponential moving standardization.
filterbank(raw, frequency_bands[, ...])	Applies multiple bandpass filters to the signals in raw.
preprocess(concat_ds, preprocessors[, ...])	Apply preprocessors to a concat dataset.
Preprocessor(fn, *[, apply_on_array])	Preprocessor for an MNE Raw or Epochs object.
Resample([up, down, npad, axis, window, ...])	Resample an array.
DropChannels(ch_names[, on_missing])	Drop channel(s).
SetEEGReference([ref_channels, projection, ...])	Specify which reference to use for EEG data.
Filter(l_freq, h_freq[, picks, ...])	Filter a subset of channels.
Pick(picks[, exclude, verbose])	Pick a subset of channels.
Crop([tmin, tmax, include_tmax, verbose])	Crop raw data file.

Data Utils

braindecode.datautil:

save_concat_dataset(path, concat_dataset[, ...])
load_concat_dataset(path, preload[, ...])	Load a stored BaseConcatDataset of BaseDatasets or WindowsDatasets from files.

Samplers

braindecode.samplers:

RecordingSampler(metadata[, random_state])	Base sampler simplifying sampling from recordings.
SequenceSampler(metadata, n_windows, ...[, ...])	Sample sequences of consecutive windows.
RelativePositioningSampler(metadata, ...[, ...])	Sample examples for the relative positioning task from [R0467437a2408-Banville2020].
BalancedSequenceSampler(metadata, n_windows)	Balanced sampling of sequences of consecutive windows with categorical targets.

Augmentation

braindecode.augmentation:

Transform([probability, random_state])	Basic transform class used for implementing data augmentation operations.
IdentityTransform([probability, random_state])	Identity transform.
Compose(transforms)	Transform composition.
AugmentedDataLoader(dataset[, transforms, ...])	A base dataloader class customized to applying augmentation Transforms.
TimeReverse(probability[, random_state])	Flip the time axis of each input with a given probability.
SignFlip(probability[, random_state])	Flip the sign axis of each input with a given probability.
FTSurrogate(probability[, ...])	FT surrogate augmentation of a single EEG channel, as proposed in [Ra7c6c14d9bd9-1].
ChannelsShuffle(probability[, p_shuffle, ...])	Randomly shuffle channels in EEG data matrix.
ChannelsDropout(probability[, p_drop, ...])	Randomly set channels to flat signal.
GaussianNoise(probability[, std, random_state])	Randomly add white noise to all channels.
ChannelsSymmetry(probability, ordered_ch_names)	Permute EEG channels inverting left and right-side sensors.
SmoothTimeMask(probability[, ...])	Smoothly replace a randomly chosen contiguous part of all channels by zeros.
BandstopFilter(probability, sfreq[, ...])	Apply a band-stop filter with desired bandwidth at a randomly selected frequency position between 0 and max_freq.
FrequencyShift(probability, sfreq[, ...])	Add a random shift in the frequency domain to all channels.
SensorsRotation(probability, ...[, axis, ...])	Interpolates EEG signals over sensors rotated around the desired axis with an angle sampled uniformly between -max_degree and max_degree.
SensorsZRotation(probability, ordered_ch_names)	Interpolates EEG signals over sensors rotated around the Z axis with an angle sampled uniformly between -max_degree and max_degree.
SensorsYRotation(probability, ordered_ch_names)	Interpolates EEG signals over sensors rotated around the Y axis with an angle sampled uniformly between -max_degree and max_degree.
SensorsXRotation(probability, ordered_ch_names)	Interpolates EEG signals over sensors rotated around the X axis with an angle sampled uniformly between -max_degree and max_degree.
Mixup(alpha[, beta_per_sample, random_state])	Implements Iterator for Mixup for EEG data.
functional.identity(X, y)	Identity operation.
functional.time_reverse(X, y)	Flip the time axis of each input.
functional.sign_flip(X, y)	Flip the sign axis of each input.
functional.ft_surrogate(X, y, ...[, ...])	FT surrogate augmentation of a single EEG channel, as proposed in [R52a4658fffa7-1].
functional.channels_dropout(X, y, p_drop[, ...])	Randomly set channels to flat signal.
functional.channels_shuffle(X, y, p_shuffle)	Randomly shuffle channels in EEG data matrix.
functional.channels_permute(X, y, permutation)	Permute EEG channels according to fixed permutation matrix.
functional.gaussian_noise(X, y, std[, ...])	Randomly add white Gaussian noise to all channels.
functional.smooth_time_mask(X, y, ...)	Smoothly replace a contiguous part of all channels by zeros.
functional.bandstop_filter(X, y, sfreq, ...)	Apply a band-stop filter with desired bandwidth at the desired frequency position.
functional.frequency_shift(X, y, delta_freq, ...)	Adds a shift in the frequency domain to all channels.
functional.sensors_rotation(X, y, ...)	Interpolates EEG signals over sensors rotated around the desired axis with the desired angle.
functional.mixup(X, y, lam, idx_perm)	Mixes two channels of EEG data.

Utils

braindecode.util:

set_random_seeds(seed, cuda[, cudnn_benchmark])

Set seeds for python random module numpy.random and torch.

Visualization

braindecode.visualization:

compute_amplitude_gradients(model, dataset, ...)
plot_confusion_matrix(confusion_mat[, ...])	Generates a confusion matrix with additional precision and sensitivity metrics as in [R8046536b33dd-1].