What’s new

Please follow the examples below like braindecode.EEGClassifier, so the whats_new page will have a link to the function/class documentation.

• “Enhancements” for new features

• “Bugs” for bug fixes

• “API changes” for backward-incompatible changes

Current 0.9 (dev0)

Enhancements

Bugs

API changes

Current 0.8 (11-2022)

Enhancements

• Adding braindecode.models.EEGInceptionMI network for motor imagery (#428 by Cedric Rommel)

• Adding braindecode.models.ATCNet network for motor imagery (#429 by Cedric Rommel)

• Adding to braindecode.datasets.tuh.TUH compatibility with version 3.0 of TUH dataset (#431 by Mohammad Javad D, Bruno Aristimunha, Robin Tibor Schirrmeister, Lukas Gemein, Denis A. Engemann and Oskar Størmer)

• Adding braindecode.models.DeepSleepNet network for sleep staging (#417 by Theo Gnassounou)

• Adding braindecode.models.EEGConformer network (#454 by Yonghao Song and Bruno Aristimunha)

• Adding einops in the requirements (#466 by Bruno Aristimunha)

• Have moabb as an extra dependency (#467 by Marco Zamboni)

• Replacing the replacing Pytorch layers to Rearrange from einops #468 (#468 by Bruno Aristimunha)

• Refactoring the documentation and creating a sub-structure for the examples (#470 by Denis A. Engemann and Bruno Aristimunha)

• Solving issues with slow conda and splitting the doc and test .yml to speed the CI. (#479 by Bruno Aristimunha)

• Improving the GitHub Actions CI and solving the skorch compatibility in the examples (#472 by Bruno Aristimunha)

• Changing the documentation order (#489 by Bruno Aristimunha)

• Improve the documentation for the Temple University Hospital (TUH) EEG Corpus with discrete targets (#485 by Pierre Guetschel and Bruno Aristimunha)

• Improving documentation for MOABB dataset, Trialwise Decoding & Cropped Decoding (#490 by Daniel Wilson)

• Improving the documentation for the sleep stage examples (#487 by Bruno Aristimunha)

• Improving the tutorial Hyperparameter tuning with scikit-learn (#473 by Bruno Aristimunha)

• Add braindecode.models.base.EEGModuleMixin base class for all braindecode models (#488 by Pierre Guetschel)

• Normalize all models common parameters and leaving the old ones as deprecated (#488 by Pierre Guetschel)

• Improving the tutorial with a Data Augmentation Search (#495 by Sylvain Chevallier)

• Improving documentation for “Split Dataset” and “Process a big data EEG resource” examples (#494 by Bruna Lopes)

• Improving documentation for the Convolutional neural network regression model on fake data (#491 by Sara Sedlar)

• Enforcing the eval mode in the function predict trial. (#497 by Bruno Aristimunha)

• Adding extra requirements for pip install, update doc, removing conda env file (#505 by Sylvain Chevallier)

• Add models user-friendly representation with torchinfo tables to braindecode.models.base.EEGModuleMixin (#488 by Maciej Śliwowski)

• Merged temporal and spatial convolutions for Deep4 and ShallowFBCSP (by Daniel Wilson and Sara Sedlar)

• Enabling data augmentation of single inputs (with no batch dimension). (#503 by Cedric Rommel)

• Adding randomize parameter to braindecode.samplers.SequenceSampler (#504 by `Théo Gnassounou`_.)

• Creating new preprocessor objects based on mne’s raw/Epochs methods braindecode.preprocessing.Resample, braindecode.preprocessing.DropChannels, braindecode.preprocessing.SetEEGReference, braindecode.preprocessing.Filter, braindecode.preprocessing.Pick, braindecode.preprocessing.Crop (#500 by Bruna Lopes and Bruno Aristimunha)

• Moving :function:`braindecode.models.util.get_output_shape` and :function:`braindecode.models.util.to_dense_prediction_model` to braindecode.models.base.EEGModuleMixin (#514 by Maciej Śliwowski)

• Automatically populate signal-related parameters in braindecode.EEGClassifier and braindecode.EEGRegressor (#517 by Pierre Guetschel)

• Adding a pure PyTorch tutorial (#523 by Remi Delbouys and Bruno Aristimunha)

• Add models_dict to braindecode.models.util (#524 by Pierre Guetschel)

• Keep using mne.Raw after windowing to speed up windowing, do not create mne.Epochs (#515 by Robin Tibor Schirrmeister)

• Changing braindecode.models.Deep4Net final_conv_length default value to ‘auto’ (#535 by Maciej Śliwowski)

• Add support for mne.Epochs in braindecode.EEGClassifier and braindecode.EEGRegressor (#529 by Pierre Guetschel)

• Allow passing only the name of a braindecode model to braindecode.EEGClassifier and braindecode.EEGRegressor (#528 by Pierre Guetschel)

• Standardizing models’ last layer names (#520 by Bruna Lopes and Pierre Guetschel)

• Add basic training example with MNE epochs (#539 by Pierre Guetschel)

• Log validation accuracy in braindecode.EEGClassifier (#541 by Pierre Guetschel)

• Better type hints in braindecode.augmentation.base (#551 by Valentin Iovene)

• Support for MOABB 1.0.0 and switch to pyproject.toml (#553 by Sylvain Chevallier)

• Adding pre-commit hooks (#556 by Bruno Aristimunha)

Bugs

• Fixing conda env in the CI (#461 by Bruno Aristimunha)

• Fixing E231 missing whitespace after ‘,’ untraceable error in old flake8 (#460 by Bruno Aristimunha)

• Removing deprecation warning due to torch transposition in braindecode.augmentation.functional._frequency_shift() (#446 by Matthieu Terris)

• Fix padding’s device in braindecode.models.EEGResNet (#451 by Pierre Guetschel)

• Fix skorch version issue (#465 by Marco Zamboni)

• Fix wrong kernel_size dtype when running torchinfo in braindecode.models.USleep (#538 by Maciej Śliwowski)

• Fix bug when using GPU and channel shuffle transform (#546 by Robin Tibor Schirrmeister)

API changes

• Renaming the braindecode.models.EEGInception network as braindecode.models.EEGInceptionERP (#428 by Cedric Rommel)

• Removing support for Python 3.7 (#397 by Bruno Aristimunha)

• Removing the LogSoftmax layer from the models and adding deprecated warnings and temporary flags (#513 by Sara Sedlar)

Current 0.7 (10-2022)

Enhancements

• Adding EEG-Inception Network braindecode.models.EEGInception (#390 by Bruno Aristimunha and Cedric Rommel)

• Adding EEG-ITNet Network braindecode.models.EEGITNet (#400 by Ghaith Bouallegue)

• Allowing target_names as list for BaseDataset (#371 by Mohammad Javad D and Robin Tibor Schirrmeister)

• Adding tutorial with GridSearchCV for data augmentation on the BCIC IV 2a with module braindecode.augmentation (#389 by Bruno Aristimunha and Cedric Rommel)

• Adding tutorial with GridSearchCV to exemplify how to tune hyperparameters, for instance with the learning rate (#349 by Lukas Gemein and by Bruno Aristimunha)

• Adding tutorial with a Unified Validation scheme (#378 by Bruno Aristimunha and Martin Wimpff)

• Adding verbose parameter to braindecode.preprocessing.create_windows_from_events(), braindecode.preprocessing.create_windows_from_target_channels(), and braindecode.preprocessing.create_fixed_length_windows() (#391 by Lukas Gemein)

• Enable augmentation on GPU within AugmentedDataloader via a new device parameter (#406 by Martin Wimpff, Bruno Aristimunha and Cedric Rommel)

• Adding randomize parameter to braindecode.samplers.SequenceSampler (#504 by `Théo Gnassounou`_.)

Bugs

• Fixing parameter subject_ids to recoding_ids in TUHAbnormal example (#402 by Bruno Aristimunha and Lukas Gemein)

• Bug fix braindecode.augmentation.functional.ft_surrogate() and add option to sample independently per-channel (#409 by Martin Wimpff and Cedric Rommel)

API changes

• Renaming the method get_params to get_augmentation_params in augmentation classes. This makes the Transform module compatible with scikit-learn cloning mechanism (#388 by Bruno Aristimunha and Alex Gramfort)

• Delaying the deprecation of the preprocessing scale function braindecode.preprocessing.scale() and updates tutorials where the function were used. (#413 by Bruno Aristimunha)

• Removing deprecated functions and classes braindecode.preprocessing.zscore(), braindecode.datautil.MNEPreproc and braindecode.datautil.NumpyPreproc (#415 by Bruno Aristimunha)

• Setting iterator_train__drop_last=True by default for braindecode.EEGClassifier and braindecode.EEGRegressor (#411 by Robin Tibor Schirrmeister)

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, Daniel 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, Daniel 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