What’s new

Version 1.4 (Source - GitHub)

Enhancements

• Add an optional lazy init path to braindecode.datasets.base.BaseConcatDataset to defer cumulative size computation (and any downstream dataset length access) until first access, supporting truly lazy raw loading workflows.

• Adding braindecode.models.EEGPT with HuggingFace Hub integration and pretrained weights available at braindecode/eegpt-pretrained (#908 by Young Truong and Kuntal Kokate)

• Add braindecode.models.CBraMod model (#914 by Pierre Guetschel)

• Expose additional arguments of mne_bids.read_raw_bids() in braindecode.datasets.BIDSDataset (#918 by Pierre Guetschel)

• Populate epochs metadata with the mne.Annotations’s extras entries (#918 by Pierre Guetschel)

• Add braindecode.datasets.TUHEvents dataset and improve other TUH datasets (#920 and #923 by Pierre Guetschel)

• Allow overlapping events in braindecode.preprocessing.create_windows_from_events() (#923 by Pierre Guetschel)

• Better alignment of channels configuration with the pretrained LABRAM model, including a warning when initializing without channel information and improved documentation (#931 by Young Truong )

• Add informative __repr__ and _repr_html_ to braindecode.datasets.RawDataset, braindecode.datasets.base.EEGWindowsDataset, braindecode.datasets.base.WindowsDataset, and braindecode.datasets.BaseConcatDataset so that printing or displaying a dataset in a notebook shows channel count/types, sampling frequency, duration/window size, channel names, description summary, and epoch-level metadata including target distribution and extra metadata columns (signal-level details marked * are taken from the first recording).

• Add mapping, preload, picks, drop_bad_windows, and descriptions parameters to braindecode.datasets.mne.create_from_mne_epochs() to bring it to parity with braindecode.datasets.mne.create_from_mne_raw() (#941 by Aman Srivastava).

• Per-event-type windowing parameters in braindecode.preprocessing.windowers.create_windows_from_events() (#942 by Pierre Guetschel)

• Add use_mne_epochs support to braindecode.preprocessing.windowers.create_fixed_length_windows() (#943 by Pierre Guetschel)

• Faster loading from epochs on disk with braindecode.datasets.base.WindowsDataset (#944 by Pierre Guetschel)

• Add possibility to convert braindecode.datasets.base.EEGWindowsDataset to braindecode.datasets.base.WindowsDataset for efficient storage (#952 by Pierre Guetschel)

• Add encoder_only mode to braindecode.models.BENDR for 4-chunk temporal pooling (Kostas et al. 2021, Section 2.4), and n_chans_pretrained parameter with max-norm constrained channel projection for fine-tuning pretrained BENDR on datasets with arbitrary channel counts (by Kuntal Kokate)

API changes

• None yet.

Requirements

• None yet.

Bugs

• Fix multi-target channel windowing in braindecode.preprocessing.windowers.create_windows_from_target_channels() to use the union of valid target positions across all misc channels instead of only the first channel (by Sarthak Tayal)

• Fix braindecode.preprocessing.preprocess.filterbank() to preserve info fields (description, line_freq, device_info, etc.) when creating filtered copies, avoiding merge conflicts in MNE when adding channels (#928 by Bruno Aristimunha)

• [Outdated:] Restrict to ``pandas>=3.0`` due to incompatibility with ``wfdb`` (#919 by Pierre Guetschel)

• Fix multiple bugs in Labram positional encoding. Now the braindecode implementation is aligned with the original one (#931 by Pierre Guetschel )

• Fix Zenodo citation: update to global concept DOI and add BibTeX/APA citation formats in docs/cite.rst, README.rst, CITATION.cff, and docs/conf.py (#937 by Bruno Aristimunha)

• Push large datasets to HuggingFace Hub using huggingface_hub.upload_large_folder() to avoid limitations, and allow resuming downloads (#945 and #953 by Pierre Guetschel)

Code health

• Remove deprecated torch.irfft fallback in braindecode.visualization.gradients.compute_amplitude_gradients_for_X(), now uses torch.fft.irfft directly since braindecode requires torch>=2.2 (by Sarthak Tayal)

Current 1.3.2 (stable)

Enhancements

• Add an optional lazy init path to braindecode.datasets.base.BaseConcatDataset to defer cumulative size computation (and any downstream dataset length access) until first access, supporting truly lazy raw loading workflows.

• Adding Hugging Face Hub integration documentation with comprehensive examples for loading pre-trained models (braindecode.models.BIOT, braindecode.models.Labram, braindecode.models.REVE, braindecode.models.LUNA, braindecode.models.BENDR, braindecode.models.SignalJEPA) and pushing models to the Hub (#879 by Bruno Aristimunha)

• Adding braindecode.models.REVE with comprehensive documentation covering architecture details, 4D positional encoding, and pretraining scale (#866 by Jonathan Lys )

• Adding augmentation braindecode.augmentation.AmplitudeScale (#848 by Bruna Lopes)

• Adding augmentation braindecode.augmentation.ChannelsReref (#848 by Bruna Lopes)

• Added Patched Brain Transformer from Klein T et al 2025 at braindecode.models.PBT (#787 by José Mauricio)

• Including typing in the augmentation module (#709 by Aphel)

• Adding braindecode.models.SSTDPN (#790 by Can Han and Bruno Aristimunha )

• Adding braindecode.models.BrainModule (#743 by Bruno Aristimunha )

• Added braindecode.models.BENDR (#802 by Bruno Aristimunha )

• Adding a tutorial for fine-tuning a pre-trained foundation model (#794 by Pierre Guetschel)

• Added 25 new MNE preprocessing functions, expanding from 6 to 31 total preprocessing classes. New functions include: braindecode.preprocessing.NotchFilter, braindecode.preprocessing.SavgolFilter, braindecode.preprocessing.ApplyHilbert, braindecode.preprocessing.InterpolateBads, braindecode.preprocessing.InterpolateTo, braindecode.preprocessing.RenameChannels, braindecode.preprocessing.ReorderChannels, braindecode.preprocessing.EqualizeChannels, braindecode.preprocessing.AddChannels, braindecode.preprocessing.SetMontage, braindecode.preprocessing.AddReferenceChannels, braindecode.preprocessing.AddProj, braindecode.preprocessing.ApplyProj, braindecode.preprocessing.DelProj, braindecode.preprocessing.ComputeCurrentSourceDensity, braindecode.preprocessing.FixStimArtifact, braindecode.preprocessing.CropByAnnotations, braindecode.preprocessing.Anonymize, braindecode.preprocessing.SetAnnotations, braindecode.preprocessing.SetChannelTypes, braindecode.preprocessing.SetMeasDate, braindecode.preprocessing.FixMagCoilTypes, braindecode.preprocessing.ApplyGradientCompensation, braindecode.preprocessing.AddEvents, and braindecode.preprocessing.Rescale (#801 by Bruno Aristimunha)

• Adding artifact removal preprocessing steps, incl. braindecode.preprocessing.EEGPrep (#796 by Christian Kothe)

• Including huggingface hub mixin to all models (#795 by Kuntal Kokate )

• Better inheritance between the different dataset classes (#806 by Pierre Guetschel )

• Fix minor documentation issues in Labram (#808 by Matthew Chen)

• Including missed tag in all the models and creating some test for it (#811 by Bruno Aristimunha )

• Added braindecode.models.LUNA (#802 by Bruno Aristimunha, Thorir Mar Ingolfsson)

• Improve braindecode.preprocessing.Preprocessor serialisation (#813 by Pierre Guetschel )

• Modify verbosity of create_window_from_events (#814 by Matthew Chen)

• Including SIENA dataset and CHB_MIT datasets (#818 by Jonathan Dan and Bruno Aristimunha )

• Modify the interface of FBMSNet for consistency with FBCNet + Unit Tests (#819 by Matthew Chen)

• Adding braindecode.models.MEDFormer (#821 by Bruno Aristimunha )

• Adding braindecode.models.EEGSym model (#697 by Pierre Guetschel and Bruno Aristimunha )

• Adding tutorial for braindecode.preprocessing.windowers.create_fixed_length_windows() (#746 by Aniela Bulicz and Arina Schippers and Aron van Zuijlen)

• Including interface with Hugging Face Hub for dataset sharing and loading (#820 by Kuntal Kokate and Bruno Aristimunha )

• Adding a CITATION.cff file, allowing researchers to directly cite the Braindecode library via a Zenodo DOI (#847 and #872 by Pierre Guetschel )

• Add automatically-generated pydantic models configs (#850 by Pierre Guetschel )

• Refactor: standardized parameter names in transformer and LabRam modules (#850 by José Mauricio)

• Added a tutorial for temporal generalization (#857 by Matthew Chen)

• Including colab buttons to all tutorials (#861 by Bruno Aristimunha )

• Adding example Pydantic and Exca (#858 by Pierre Guetschel)

• Renaming the model category “Foundation Models” from “Large Brain Models (LBM)” (#867 by Bruno Aristimunha )

• Make the dataset at huggingface hub more bids compatible (#871 by Bruno Aristimunha )

• Reorganized BIDS-related modules into braindecode.datasets.bids subpackage for better code organization. The subpackage includes: dataset loading (datasets.py), Hub format utilities (hub_format.py), Hub integration (hub.py), and validation utilities (hub_validation.py) (#871 by Bruno Aristimunha)

• Changed metadata storage in Zarr format from JSON attributes to TSV files (metadata.tsv) for better scalability with large datasets (#871 by Bruno Aristimunha)

• Improved JSON serialization in zarr.json by storing description and info as proper JSON objects instead of stringified JSON, with NaN/Inf sanitization for valid JSON output (#871 by Bruno Aristimunha)

API changes

• BIDS and Hub modules moved to braindecode.datasets.bids subpackage: braindecode.datasets.bids.hub, braindecode.datasets.bids.hub_format, braindecode.datasets.bids.datasets, braindecode.datasets.bids.hub_validation (#871 by Bruno Aristimunha)

• Deprecating the old naming of MOABB Dataset name (#826 by Bruno Aristimunha)

• Exposing the braindecode.datautil.infer_signal_properties() utility function (#856 by Pierre Guetschel)

• Deprecating the old naming of MOABB Dataset name #826 by Bruno Aristimunha

• Drop support for Python 3.10 and increase support to Python 3.13 and python 3.14 (#840 by Bruno Aristimunha)

  • Model config helpers now soft-import pydantic/numpydantic; if the optional dependencies are missing the module skips config generation and warns to install pip install braindecode[pydantic].

Bugs

• Fixing the issues with braindecode.models.ATCNet (#782 by Hamdi Altaheri)

• Fixing expired path when loading a preprocessed dataset that was moved (#786 by Pierre Guetschel)

• Fix OverflowError sometimes when saving description to a json file (#789 by Pierre Guetschel)

• Small adjustment in braindecode.models.Labram model to allow pre-loading of weights (#792 by Bruno Aristimunha)

• Fixing many warnings for generating the documentation (#805 by Bruno Aristimunha)

• Fix test compatibility with scikit-learn 1.8+ by adding __sklearn_tags__ support to test mocks (#868 by Bruno Aristimunha)

• Increase the mne-bids version and fixing API changes (#907 by Bruno Aristimunha)

Current 1.2

Enhancements

• Improving the docstring for braindecode.models.EEGNetv4 (#768 by Bruno Aristimunha)

• Improving the docstring for braindecode.models.EEGConformer (#769 by Bruno Aristimunha)

• Experimental BIDS Iterable Dataset support (#766 by Bruno Aristimunha and Pierre Guetschel)

• Improving the docstring for braindecode.models.ATCNet (#771 by Bruno Aristimunha)

• Improving the docstring for braindecode.models.AttentionBaseNet (#772 by Bruno Aristimunha)

• Improving the docstring for braindecode.models.EEGNeX (#773 by Bruno Aristimunha)

• Massive refactor of the models webpage (#774 by Bruno Aristimunha)

• Massive refactor of the models webpage (#775 by Bruno Aristimunha), creating the models categorization, table and many details.

API changes

• Using the name from the original name and deprecation models that we create for no reason, models #775 by Bruno Aristimunha

• Deprecated the version name in braindecode.models.EEGNetv4 in favour of braindecode.models.EEGNetv.

• Deprecated the version name in braindecode.models.SleepStagerEldele2021 in favour of braindecode.models.AttnSleep.

• Deprecated the version name in braindecode.models.TSceptionV1 in favour of braindecode.models.TSception.

Version 1.1.1

Enhancements

• Massive refactor of the model webpage

Bugs

• Fixing several models to be be compatibility with small windows size(#756 by Bruno Aristimunha)

• Fix initialization of braindecode.models.Deep4Net (#752 by Bruno Aristimunha)

• Fix initialization of braindecode.models.ATCNet (#754 by Bruno Aristimunha)

• Fix Labram model for small input (#757 by Bruno Aristimunha)

• Fix the CTNet parameters to make the embedding configuration easy (#760 by Bruno Aristimunha)

Version 1.0

Enhancements

• Make sure all the models start at train model (#745 by Bruno Aristimunha)

• Enable more models to be pytorch compatible (#732 by Bruno Aristimunha and Lucas Heck)

• Making the braindecode.models compatibility with torch compile, torch export and torch jit (#729 by Bruno Aristimunha and Pierre Guetschel)

• Reorder the modules, functional and re-organize the codebase (#728 by Bruno Aristimunha)

• Adding braindecode.models.IFNet (#725 by Bruno Aristimunha )

• Adding braindecode.models.FBMSNet (#724 by Bruno Aristimunha )

• Adding braindecode.models.FBCNet (#722 by Bruno Aristimunha )

• Adding braindecode.models.FBLightConvNet (#723 by Bruno Aristimunha )

• Added dropdown menus for selecting paradigm, type, and hyperparameters on the model summary page. (#718 by Ganasekhar Kalla)

• Adding model page (#715 by Lucas Heck)

• Inference of shape for string module when using skorch.helper.SliceDataset (#716 by Bruno Aristimunha)

• Fix error when using n_njobs > 1 on Windows (#700 by Arnaud Delorme)

• Adding braindecode.models.AttentionBaseNet (#572 by Bruno Aristimunha and Martin Wimpff)

• Adding braindecode.datasets.NMT dataset (#443 by Mohammad Javad D and Bruno Aristimunha)

• Adding an integration test for all the models (:gh: 570 by Bruno Aristimunha)

• Adding braindecode.models.BIOT (#573 by Bruno Aristimunha)

• Adding braindecode.models.Labram (#578 by Bruno Aristimunha)

• Applying black to the codebase (#579 by Bruno Aristimunha)

• Adding braindecode.models.EEGSimpleConv (#581 by Yassine El Ouahidi and Bruno Aristimunha)

• Increasing the coverage of the tests (#592 by Bruno Aristimunha)

• Adding cache and pre-processing option to braindecode.datasets.MOABBDataset (#582 by Bruno Aristimunha)

• Add type hints to datasets (#590 by Pierre Guetschel)

• Add channel names and montage to braindecode.datasets.TUH (#593 by Pierre Guetschel)

• Add offset arg to braindecode.preprocessing.preprocess() (#599 by Pierre Guetschel)

• Add type hints to preprocessing (#600 by Pierre Guetschel)

• Add mypy type checks to pre-commit and CI (#606 by Pierre Guetschel)

• Code clarity changes in windowers.py (#615 by John Muradeli)

• Adding SegmentationReconstruction augmentation braindecode.augmentation.SegmentationReconstruction (#608 by Gustavo Rodrigues)

• Add two models braindecode.models.ContraWR and braindecode.models.SPARCNet (#611 by Bruno Aristimunha)

• Add Sleep Physionet 18 dataset (#621 by Hubert Banville and Bruno Aristimunha)

• Optimize the CI by executing only the last commit (#612 by Bruno Aristimunha)

• Add experimental lazy_metadata parameter to braindecode.preprocessing.create_fixed_length_windows() (#597 by Pierre Guetschel)

• Increasing moabb version to 1.1.0 (#632 by Bruno Aristimunha)

• Add MaskEncoding augmentation braindecode.augmentation.MaskEncoding (#631 by Gustavo Rodrigues)

• Adding braindecode.models.EEGNex (#635 by Bruno Aristimunha )

• Adding braindecode.models.TSception (#641 by Bruno Aristimunha )

• Adding braindecode.models.EEGTCNet (#640 by Bruno Aristimunha )

• Ensure consistency in the last layer using tests (#642 by Bruno Aristimunha )

• Ensuring consistency on the expose of the activation function (#637 by Bruno Aristimunha )

• Adding braindecode.models.SyncNet (#643 by Bruno Aristimunha )

• Adding braindecode.models.MSVTNet (#659 by Bruno Aristimunha )

• Creating the FilterBanklayer module for new models (#656 by Bruno Aristimunha )

• Including PytorchAudio as dependency and remove copied code (#661 by Bruno Aristimunha)

• Adding braindecode.models.EEGMiner (#667 by Bruno Aristimunha )

• Adding braindecode.models.CTNet (#666 by Bruno Aristimunha )

• Fix warnings not being suppressed when creating a TUHAbnormal dataset in parallel (#670 by Aphel)

• Exposing braindecode.models.EEGITNet hyper-parameters (#672 by Bruno Aristimunha)

• Adding braindecode.models.SincShallowNet (#678 by Bruno Aristimunha )

• Adding braindecode.models.SCCNet (#679 by Bruno Aristimunha )

• Fix error when using NMT dataset with n_jobs > 1 (#690 by Aphel)

• Adding support for distributed samplers (#695 by Young Truong)

• Adding braindecode.datasets.BIDSDataset and braindecode.datasets.BIDSEpochsDataset plus tutorial (#701 #702 #704 by Pierre Guetschel )

• Add braindecode.models.SignalJEPA plus downstream architectures (#703 by Pierre Guetschel )

• Various improvements on documentation:

  • #706 by Pierre Guetschel

Bugs

• Making the braindecode.models.SCCNet more compatible with paper instead of source code (#742 by Bruno Aristimunha)

• Making the braindecode.models.EEGNeX and braindecode.models.CTNet more compatible with paper instead of source code (#740 by Bruno Aristimunha)

• Exposing extra variable to avoid problem with the parallel process (#736 by Pierre Guetschel)

• Fixing the IFNet (#739 by Bruno Aristimunha)

• Fix dead links in the documentation (#727 by Lucas Heck)

• Fix annotations bug for moabb datasets with non-zero interval (:gh: 561 by Daniel Wilson)

• Fix deprecated test and changing the what’s new checker (:gh: 569 by Bruno Aristimunha)

• Fix issue with coverage CI and adding a condition on the test for avoid HTML errors (:gh: 591 by Bruno Aristimunha)

• Constraint the version of mne (:gh: 594 by Bruno Aristimunha)

• Fix type errors (#606 by Pierre Guetschel)

• Warn when applying preprocessing steps on a braindecode.datasets.base.EEGWindowsDataset (#607 by Pierre Guetschel)

• Fix matplotlib colormaps deprecation (#608 by Bruno Aristimunha)

• Ensure mypy to work for every commit (#619 by Bruno Aristimunha)

• Deprecate moabb version 1.0.0 because of incorrect epoching (#627 by Pierre Guetschel)

• Fixing tutorial benchmark lazy eager loagin (:gh:`` by Bruno Aristimunha and Aphel)

• Improve doc build’s time with better caching (#693 by Thomas Moreau)

• Fixing the MOABBDataset to work with the cache (#694 by Bruno Aristimunha)

API changes

• Removing deprecated variable use_log_softmax from braindecode.models.base (#729 by Bruno Aristimunha)

• Removing function squeze_final_output with Expression because of not compatibility with torch script (#729 by Bruno Aristimunha)

• Expose the use_mne_epochs parameter of braindecode.preprocessing.create_windows_from_events() (#607 by Pierre Guetschel)

• Parameter use_log_softmax is default as False for all the models in (#624 by Bruno Aristimunha)

• Normalizing the parameters for dropout as part of normalization of model parameters (#624 by Bruno Aristimunha)

• Removing use_log_softmax and old parameters (#671 by Bruno Aristimunha)

• Moving braindecode.models.TCN and braindecode.models.HybridNet to module and creating braindecode.models.BDTCN (#673 by Bruno Aristimunha)

• Removing dead parameters from braindecode.models.EEGNetv4 (#676 by Bruno Aristimunha)

• Including Linear Layer at the end braindecode.models.EEGNetv4 (#680 by Bruno Aristimunha)

• Expose copy_data parameter in braindecode.preprocessing.preprocess() (#736 by Pierre Guetschel)

Version 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 Théo 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 braindecode.models.util.get_output_shape and 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)

Version 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, Daniel 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 Théo 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 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 Daniel 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 Théo 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)