What’s new

Version 0.6 (2021-12-06)

Enhancements

• Adding braindecode.samplers.SequenceSampler along with support for returning sequences of windows in braindecode.datasets.BaseConcatDataset and an updated sleep staging example to show how to train on sequences of windows (#263 by Hubert Banville)

• Adding Thinker Invariance Network braindecode.models.TIDNet (#170 by Ann-Kathrin Kiessner, Dan Wilson, Henrik Bonsmann, Vytautas Jankauskas)

• Adding a confusion matrix plot generator braindecode.visualization.plot_confusion_matrix() (#274 by Ann-Kathrin Kiessner, Dan Wilson, Henrik Bonsmann, Vytautas Jankauskas)

• Adding data Augmentation module (#254 by Cedric Rommel, Alex Gramfort and Thomas Moreau)

• Adding Mixup augmentation braindecode.augmentation.Mixup (#254 by Simon Brandt)

• Adding saving of preprocessing and windowing choices in braindecode.preprocessing.preprocess(), braindecode.preprocessing.create_windows_from_events() and braindecode.preprocessing.create_fixed_length_windows() to datasets to facilitate reproducibility (#287 by Lukas Gemein)

• Adding braindecode.models.util.aggregate_probas() to perform self-ensembling of predictions with sequence-to-sequence models (#294 by Hubert Banville)

• Adding braindecode.training.scoring.predict_trials() to generate trialwise predictions after cropped training (#312 by Lukas Gemein)

• Preprocessing and windowing choices are now saved on the level of individual datasets (#288 by Lukas Gemein)

• Serialization now happens entirely on dataset level creating subsets for individual datasets that contain ‘fif’ and ‘json’ files (#288 Lukas Gemein)

• Instantiation of TUH braindecode.datasets.tuh.TUH and TUHAbnormal braindecode.datasets.tuh.TUHAbnormal, as well as loading braindecode.datautil.serialization.load_concat_dataset() of stored datasets now support multiple workers (#288 by Lukas Gemein)

• Adding balanced sampling of sequences of windows with braindecode.samplers.BalancedSequenceSampler as proposed in U-Sleep paper (#295 by Theo Gnassounou and Hubert Banville)

• braindecode.preprocessing.preprocess() can now work in parallel and serialize datasets to enable lazy-loading (i.e. preload=False) (#277 by Hubert Banville)

• Adding braindecode.models.TimeDistributed to apply a module on a sequence (#318 by Hubert Banville)

• Adding time series targets decoding together with braindecode.datasets.BCICompetitionIVDataset4 and fingers flexion decoding from ECoG examples (#261 by Maciej Śliwowski and Mohammed Fattouh)

• Make EEGClassifier and EEGRegressor cloneable for scikit-learn (#347 by Lukas Gemein, Robin Tibor Schirrmeister, Maciej Śliwowski and Alex Gramfort)

• Allow to raise a warning when a few trials are shorter than the windows length, instead of raising an error and stopping all computation. (#353 by Cedric Rommel)

• Setting torch.backends.cudnn.benchmark in braindecode.util.set_random_seeds(), adding warning and more info to the docstring to improve reproducibility (#333 by Maciej Śliwowski)

• Adding option to pass arguments through braindecode.datasets.MOABBDataset (#365 by Pierre Guetschel)

• Adding a possibility to use a dict to split a BaseConcatDataset in braindecode.datasets.BaseConcatDataset.split() (#367 by Alex Gramfort)

• Adding crop parameter to braindecode.datasets.SleepPhysionet dataset to speed up examples (#367 by Alex Gramfort)

Bugs

• Correctly computing recording length in braindecode.preprocessing.windowers.create_fixed_length_windows() in case recording was cropped (#304 by Lukas Gemein)

• Fixing braindecode.datasets.SleepPhysionet to allow serialization and avoid mismatch in channel names attributes (#327 by Hubert Banville)

• Propagating target_transform to all datasets when using braindecode.datasets.BaseConcatDataset.subset() (#261 by Maciej Śliwowski)

API changes

• Removing the default sampling frequency sfreq value in braindecode.datasets.create_windows_from_events() (#256 by Ann-Kathrin Kiessner, Dan Wilson, Henrik Bonsmann, Vytautas Jankauskas)

• Made windowing arguments optional in braindecode.preprocessing.windowers.create_fixed_length_windows() & braindecode.preprocessing.windowers.create_windows_from_events() (#269 by Ann-Kathrin Kiessner, Dan Wilson, Henrik Bonsmann, Vytautas Jankauskas)

• Deprecating preprocessing functions braindecode.preprocessing.zscore() and braindecode.preprocessing.scale() in favour of sklearn’s implementation (#292 by Hubert Banville)

• braindecode.preprocessing.preprocess() now returns a braindecode.dataset.BaseConcatDataset object (#277 by Hubert Banville)

Version 0.5.1 (2021-07-14)

Enhancements

• Adding n_jobs parameter to windowers braindecode.datautil.create_windows_from_events() and braindecode.datautil.create_fixed_length_windows() to allow for parallelization of the windowing process (#199 by Hubert Banville)

• Adding support for on-the-fly transforms (#198 by Hubert Banville)

• Unifying preprocessors under the braindecode.datautil.Preprocessor class (#197 by Hubert Banville)

• Adding self-supervised learning example on the Sleep Physionet dataset along with new sampler module braindecode.samplers (#178 by Hubert Banville)

• Adding sleep staging example on the Sleep Physionet dataset (#161 by Hubert Banville)

• Adding new parameters to windowers braindecode.datautil.create_windows_from_events() and braindecode.datautil.create_fixed_length_windows() for finer control over epoching (#152 by Hubert Banville)

• Adding Temporal Convolutional Network braindecode.models.TCN (#138 by Lukas Gemein)

• Adding option to use BaseConcatDataset as input to BaseConcatDataset (#142 by Lukas Gemein)

• Adding a simplified API for splitting of BaseConcatDataset: parameters property and split_ids in braindecode.datasets.BaseConcatDataset.split() are replaced by by (#147 by Lukas Gemein)

• Adding a preprocessor that realizes a filterbank: braindecode.datautil.filterbank() (#158 by Lukas Gemein)

• Removing code duplicate in BaseDataset and WindowsDataset (#159 by Lukas Gemein)

• Only load data if needed during preprocessing (e.g., allow timecrop without loading) (#164 by Robin Tibor Schirrmeister)

• Adding option to sort filtered channels by frequency band for the filterbank in braindecode.datautil.filterbank() (#185 by Lukas Gemein)

• Adding the USleep model braindecode.models.USleep (#282 by Theo Gnassounou and Omar Chehab)

• Adding braindecode.models.SleepStagerEldele2021 and braindecode.models.SleepStagerBlanco2020 models for sleep staging (#341 by Divyesh Narayanan)

Bugs

• Amplitude gradients are correctly computed for layers with multiple filters (before, they were accidentally summed over all previous filters in the layer) (#167 by Robin Tibor Schirrmeister)

• braindecode.models.get_output_shape() and braindecode.visualization.compute_amplitude_gradients() assume 3d, not 4d inputs (#166 by Robin Tibor Schirrmeister)

• Fixing windower functions when the continuous data has been cropped (#152 by Hubert Banville)

• Fixing incorrect usage of recording ids in TUHAbnormal (#146 by Lukas Gemein)

• Adding check for correct input dimensions (4d) in TCN (#169 by Lukas Gemein)

• Fixing braindecode.datautil.create_windows_from_events() when window_size is not given but there is a trial_stop_offset_samples (#148 by Lukas Gemein)

• Fixing braindecode.classifier.EEGClassifier.predict_proba() and braindecode.regressor.EEGRegressor.predict() behavior in the cropped mode (#171 by Maciej Śliwowski)

• Freeze torch random generator for scoring functions for reproducibility (#155 by Robin Tibor Schirrmeister)

• Make EEGResNet work for final_pool_length='auto' (#223 by Robin Tibor Schirrmeister and Maciej Śliwowski)

API changes

• Preprocessor classes braindecode.datautil.MNEPreproc and braindecode.datautil.NumpyPreproc are deprecated in favor of braindecode.datautil.Preprocessor (#197 by Hubert Banville)

• Parameter stop_offset_samples of braindecode.datautil.create_fixed_length_windows() must now be set to None instead of 0 to indicate the end of the recording (#152 by Hubert Banville)

Authors