API Reference

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

braindecode:

Classifier

braindecode.classifier:

EEGClassifier(*args[, cropped, callbacks, …])

Classifier that does not assume softmax activation.

Regressor

braindecode.regressor:

EEGRegressor(*args[, cropped, callbacks, …])

Regressor that calls loss function directly.

Models

braindecode.models:

ShallowFBCSPNet(in_chans, n_classes[, …])	Shallow ConvNet model from [R9432a19f6121-2].
Deep4Net(in_chans, n_classes, …[, …])	Deep ConvNet model from [Rb8ef6c2733ce-1].
EEGNetv1(in_chans, n_classes[, …])	EEGNet model from [Rcbe3da2652d4-EEGNet].
EEGNetv4(in_chans, n_classes[, …])	EEGNet v4 model from [Rbd7837e27d7f-EEGNet4].
HybridNet(in_chans, n_classes, …)	Hybrid ConvNet model from [R4d1af390a17e-3].
EEGResNet(in_chans, n_classes, …[, …])	Residual Network for EEG.
TCN(n_in_chans, n_outputs, n_blocks, …)	Temporal Convolutional Network (TCN) as described in [Rcfca6ae7a220-1].

Training

braindecode.training:

CroppedLoss(loss_function)	Compute Loss after averaging predictions across time.
CroppedTrialEpochScoring(scoring[, …])	Class to compute scores for trials from a model that predicts (super)crops.
PostEpochTrainScoring(scoring[, …])	Epoch Scoring class that recomputes predictions after the epoch on the training in validation mode.
trial_preds_from_window_preds(preds, …)	Assigning window predictions to trials while removing duplicate predictions.

Datasets

braindecode.datasets:

BaseDataset(raw[, description, target_name])	A base dataset holds a mne.Raw, and a pandas.DataFrame with additional description, such as subject_id, session_id, run_id, or age or gender of subjects.
BaseConcatDataset(list_of_ds)	A base class for concatenated datasets.
WindowsDataset(windows[, description])	Applies a windower to a base dataset.
MOABBDataset(dataset_name, subject_ids)	A class for moabb datasets.

Data Utils

braindecode.datautil:

create_from_X_y(X, y, drop_last_window[, …])	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
create_fixed_length_windows(concat_ds, …)	Windower that creates sliding windows.
create_windows_from_events(concat_ds, …[, …])	Windower that creates windows based on events in mne.Raw.
exponential_moving_demean(data[, …])	Perform exponential moving demeanining.
exponential_moving_standardize(data[, …])	Perform exponential moving standardization.
zscore(data)	Zscore continuous or windowed data in-place
scale(data, factor)	Scale continuous or windowed data in-place
save_concat_dataset(path, concat_dataset[, …])	Save a BaseConcatDataset of BaseDatasets or WindowsDatasets to files
load_concat_dataset(path, preload[, …])	Load a stored BaseConcatDataset of BaseDatasets or WindowsDatasets from files

Utils

braindecode.util:

set_random_seeds(seed, cuda)

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