What’s new

Current 1.5.0 (stable)

Enhancements

• Add braindecode.datasets.BaseConcatDataset.set_target() to swap any per-window metadata column or per-record description field (e.g. a BIDS entity, a participants.tsv extra) into the dataset’s target y in one call, replacing the manual for ds in concat.datasets: ds.metadata.loc[:, 'target'] = ...; ds.y = ... loop. Dispatches on the subdataset type: writes metadata['target'] / ds.y for windowed records, and points target_name at the chosen description field for raw records. By Bruno Aristimunha.

• Redesign the documentation landing page (docs/index.rst) in a pyhealth.dev-style layout: animated brain → EEG → net hero, fact strip highlighting MOABB / EEGDash interoperability, interactive model-zoo browser sourced from braindecode/models/summary.csv, Hugging Face Hub integration row, and a tutorial-thumbnail carousel. Adds sphinxext-opengraph to docs extras and a SoftwareApplication JSON-LD block. (#1007 by Bruno Aristimunha)

• Tutorials now train for a few epochs then load pretrained weights from Hugging Face Hub to show full training curves and metrics. All 9 tutorial checkpoints published to huggingface.co/braindecode/. The offline training script used to produce the checkpoints is available as a gist: https://gist.github.com/bruAristimunha/27d74c8410fe9d0db258a03f42efa7c6. (#985 by Bruno Aristimunha)

• Use F.scaled_dot_product_attention in braindecode.modules.MultiHeadAttention, enabling optimized attention kernels (flash-attention on CUDA, memory-efficient backends on other devices). By Léo Burgund and Bruno Aristimunha. (#902)

• Add experimental channel interpolation feature: new braindecode.modules.ChannelInterpolationLayer plus the braindecode.models.InterpolatedModel() class factory project arbitrary user channel sets to a model’s canonical set via an MNE-backed (frozen by default) interpolation matrix. Ship pre-built variants braindecode.models.InterpolatedLaBraM, braindecode.models.InterpolatedSignalJEPA, and braindecode.models.InterpolatedBIOT for the corresponding pre-trained models. (#993 by Pierre Guetschel)

• Add a tutorial walking through the experimental Interpolated* family (Loading Pretrained Foundation Models on Arbitrary Channel Sets): failure mode of the vanilla backbones on non-canonical channel sets, one-line fix via braindecode.models.InterpolatedLaBraM / braindecode.models.InterpolatedBIOT / braindecode.models.InterpolatedSignalJEPA, side-by-side visualisation of the name_match vs always interpolation matrices, and the trainable=True flag. (#994 by Pierre Guetschel)

• Mark deterministic index buffers (braindecode.models.BIOT encoder’s index and braindecode.models.REVE’s position embedding bank) as non-persistent. They are rebuilt from __init__ arguments on every instantiation, so keeping them in state_dict only bloated checkpoints and caused spurious mismatches when n_chans (or the position-bank config) differed between save and load. (#993 by Pierre Guetschel)

• Add braindecode.visualization interpretability utilities, all built on plain PyTorch autograd with no extra dependencies: saliency(), input_x_gradient(), integrated_gradients(), layer_grad_cam(), project_to_topomap() (thin wrapper around mne.viz.plot_topomap()), and compute_metrics() for quantitative attribution comparison. A new tutorial, Interpretability of EEG Decoders, walks through the full pipeline.

• Add braindecode.models.MetaNeuromotorHand, a port of the handwriting decoder from Meta / CTRL-labs’ generic neuromotor interface (Kaifosh, Reardon et al., Nature 2025). The model takes raw 16-channel surface EMG from the wristband at 2 kHz and produces per-token scores for CTC decoding of handwritten text. The pipeline is a fixed multivariate power frequency (MPF) featurizer (channel-wise STFT, cross-spectral density, frequency-band averaging, and SPD matrix logarithm) followed by a circular rotation-invariant MLP and a 15-block causal conformer encoder. Meta’s pretrained checkpoint loads directly via load_state_dict (after stripping the network. prefix); the port is bit-exact to the upstream reference implementation on real sEMG. Distributed under CC BY-NC 4.0 to match the upstream repository; see the class docstring for the license warning and the pretrained-checkpoint loading recipe. By Bruno Aristimunha.

• Add braindecode.models.EMG2QwertyNet, a port of the TDS-Conv-CTC touch-typing decoder from facebookresearch/emg2qwerty (Sivakumar et al., NeurIPS 2024). The model takes raw 32-channel surface EMG (two 16-electrode wristbands at 2 kHz) and emits per-frame scores over a 99-class typing vocabulary (98 keys + CTC blank); pass log_softmax=True to get log-probabilities directly consumable by CTCLoss. The pipeline is a parameter-free log-spectrogram front-end, per-electrode-per-band BatchNorm, a circular rotation-invariant MLP (one per band), and a stack of Time-Depth-Separable convolutional blocks (Hannun et al., 2019) without temporal padding. The encoder nn.Sequential mirrors upstream’s TDSConvCTCModule.model indices for parameter-bearing children, so upstream emg2qwerty checkpoints load directly via load_state_dict (after stripping the PyTorch-Lightning network. prefix); the class-level mapping only remaps the head from model.4.{weight,bias} to final_layer.{weight,bias}. Distributed under CC BY-NC 4.0 to match the upstream repository; see the class docstring for the license warning and the pretrained-checkpoint loading recipe. By Bruno Aristimunha.

API and behavior changes

• braindecode.modules.MultiHeadAttention now follows PyTorch’s SDPA mask convention: boolean masks use True to ignore a position (previously True meant keep). The scaling factor is now 1/sqrt(head_dim) instead of 1/sqrt(emb_size). (#902)

• braindecode.models.BENDR: remove the n_chans_pretrained / chan_proj_max_norm parameters and the channel_projection layer; hard-code the 20 pre-training channels as _BENDR_TARGET_CHS_TUPLES. The official braindecode/braindecode-bendr checkpoint has been re-uploaded flat so from_pretrained now loads its 99 weights (previously 0 of 99 matched silently). Also ships braindecode.models.InterpolatedBENDR, the InterpolatedModel() wrapper that accepts arbitrary user chs_info and projects to the canonical 20 BENDR channels (the SCALE target has no physical position, so its interpolation row is a spatial spline of the user’s EEG — not the dn3 amplitude statistic). (#992 by Pierre Guetschel)

• braindecode.models.Labram now requires chs_info to match LABRAM_CHANNEL_ORDER exactly (128 channels, canonical order). The on_unknown_chs parameter and the forward-time ch_names argument are removed. Users with arbitrary channel sets should migrate to braindecode.models.InterpolatedLaBraM. (#993 by Pierre Guetschel)

Bug fixes

• Fix braindecode.models.SyncNet swapped parameter initialization where phi_ini (phase shift) was using beta_init_values and beta (decay) was using phase_init_values, replaced incorrect .view() reshape with .permute() for proper conv2d filter weight layout, and fixed duplicate default values in docstring (by Sarthak Tayal)

• Fix braindecode.models.AttentionBaseNet redundant super().__init__() call that ran the parent nn.Module.__init__ twice (by Sarthak Tayal)

• Fix incomplete author email in braindecode.models.TSception header (by Sarthak Tayal)

• Fix a time-of-check-time-of-use race in braindecode.datasets.base._zarr_to_memmap() that caused concurrent workers to repeatedly rename-replace the published .npy cache, producing wasted I/O on local filesystems and .nfsXXXX silly-rename files plus SIGBUS crashes on NFSv3. The published file is now created exactly once via os.link and is never replaced, making the cache safe under arbitrary concurrent access on local POSIX, NFSv3, Lustre and SMB (#986 by Pierre Guetschel)

• Register braindecode.models.BIOT encoder index as a non-trainable buffer instead of a parameter (torch.long), so it is treated as module state rather than trainable weights (#988 by Pierre Guetschel)

• Fix TypeError: type 'Any' is not subscriptable when importing braindecode.models.config without numpydantic installed on Python 3.12+ (#871 by Sarthak Tayal)

• Fix braindecode.preprocessing.create_fixed_length_windows() crashing when only window_size_samples is provided without window_stride_samples, stride now defaults to window size as documented (#990 by Sarthak Tayal)

• Add channel_embedding parameter to braindecode.models.SignalJEPA and braindecode.models.SignalJEPA_Contextual to load pre-trained channel embedding weights when fine-tuning on a subset of the pre-training channels. Two new HuggingFace checkpoints are published: braindecode/signal-jepa and braindecode/signal-jepa_without-chans (#991 by Pierre Guetschel)

• Bump openneuro-py to >=2026.4.0 so the docs build picks up upstream PR #308 (DatasetFile.key → id). The previous <2026.4 pin (#1000) avoided a libc double-free seen with newer releases but broke examples/datasets_io/plot_bids_dataset_example.py against the live OpenNeuro 5.0.0 GraphQL schema (Cannot query field "key" on type "DatasetFile") (#1002 by Bruno Aristimunha)

• Retry transient TLS failures from physionet.org when fetching braindecode.datasets.SleepPhysionet (by Bruno Aristimunha)

Current 1.4.0 (stable)

Enhancements

• Unlock the SPD model category in the documentation and link to the spd_learn library with intersphinx cross-references to its seven models (SPDNet, EEGSPDNet, TSMNet, TensorCSPNet, PhaseSPDNet, Green, MAtt) (by Bruno Aristimunha)

• Hub integration docstring is now model-specific: each model’s documentation shows examples with its own class name instead of a generic EEGNetv4 reference, and existing model notes are preserved (by Bruno Aristimunha)

• Optimize braindecode.preprocessing.exponential_moving_standardize() and braindecode.preprocessing.exponential_moving_demean() by replacing Pandas-based implementation with a more efficient SciPy lfilter version, achieving a ~1.5x speedup (#950 by Léo Burgund)

• 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)

• Add braindecode.models.CodeBrain foundation model with pretrained weight loading from HuggingFace (#964 by Vandit Shah)

• 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 braindecode.models.DGCNN model for EEG emotion recognition using dynamic graph convolutional neural networks (#947 by Vandit Shah)

• 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)

• Add dynamic SDPA fallback in braindecode.models.reve.ClassicalAttention to support PyTorch versions without SDPA (by GalAshkenazi1)

• Add unified return_features parameter to forward() of all 7 foundation models (EEGPT, Labram, REVE, BENDR, BIOT, CBraMod, SignalJEPA variants). When True, returns a consistent {"features": Tensor, "cls_token": Tensor | None} dict across all models. Legacy parameters (return_encoder_output, return_feature, return_all_tokens, etc.) continue to work unchanged (by Bruno Aristimunha).

• Add reset_head() to all 7 foundation models for replacing the classification head with a new number of outputs. from_pretrained() now automatically calls reset_head when the user passes an n_outputs that differs from the saved config (by Bruno Aristimunha).

• Enable lazy loading from Zarr when using pull_from_hub() or load_from_zarr with preload=False. Data is decompressed lazily and MNE objects (.raw / .windows) are reconstructed on first access so preprocess() works on lazy datasets (#978 by Bruno Aristimunha).

API changes

• Add braindecode.models.base.EEGModuleMixin.get_config() and braindecode.models.base.EEGModuleMixin.from_config() to all models, enabling full JSON round-trip serialization and reconstruction of any model including all __init__ parameters (by Bruno Aristimunha)

• push_to_hub() now saves all model parameters to config.json (previously only 6 EEG-specific parameters were saved; model-specific parameters like F1, D, drop_prob were lost on reload) (by Bruno Aristimunha)

• Add braindecode.modules.Square activation module and update braindecode.models.ShallowFBCSPNet to use type[nn.Module] for conv_nonlin (backward-compatible with callable) (by Bruno Aristimunha)

• Replace LazyLinear with Linear in braindecode.models.CBraMod when input dimensions are known, improving Hub round-trip compatibility (by Bruno Aristimunha)

Requirements

• Relaxed PyTorch requirement to >=2.0 to support Intel-based Macs (by GalAshkenazi1)

Bugs

• Improve the error message when from_pretrained() or push_to_hub() are called without the optional huggingface_hub dependency installed. Users now get a clear ImportError with installation instructions (pip install 'braindecode[hub]') instead of an AttributeError. (#1024 by `@copilot`_)

• Fix the documentation header “Cite Braindecode” announcement link: it used a bare cite.html URL, which browsers resolve relative to the current page path and led to 404s (for example from install/install.html). The link is now built with Sphinx’s pathto() for each page so it always targets the cite page correctly.

• Fix braindecode.models.EEGITNet state dict mapping that pointed bias to the weight key and referenced a nonexistent submodule path, and fix third inception branch using the wrong variable for kernel length (by Sarthak Tayal)

• Fix braindecode.models.EEGInceptionMI state dict mapping typo where the old key was tc.bias instead of fc.bias (by Sarthak Tayal)

• 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)

• Fix channel reduction in braindecode.modules.SqueezeAndExcitation to avoid runtime shape mismatches when the reduced channel count differs from the reduction rate (#889 by Sarthak Tayal)

• 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)

• Fix braindecode.models.LUNA channel location embeddings repeated along batch dimension instead of patch dimension in prepare_tokens, and include pretrained weight typo mapping in self.mapping (#887 by Sarthak Tayal)

• Fix temporal generalization tutorial producing degraded results (peak AUC dropped from ~0.9 to ~0.75): MEG data in SI units (T/m) has variances ~1e-23, so BatchNorm1d’s eps=1e-5 dominated the normalization denominator. Now uses epochs.get_data(units="fT/cm") to bring data to a reasonable scale, and removes the misleading “importance of normalization” section whose conclusions were an artifact of the data scale issue (by Bruno Aristimunha)

• Fix braindecode.augmentation.BandstopFilter notch center frequency range using bandwidth/2 instead of 2*bandwidth to match docstring (#548 by Sarthak Tayal)

• Fix braindecode.models.DeepSleepNet hardcoded linear layer size that caused a shape mismatch when using input shapes other than the default 1 channel, 3000 timepoints. The FC and BiLSTM input dimensions are now computed dynamically from the CNN output (#755 by Sarthak Tayal)

• Fix model docstring inheritance: track_model_init_kwargs wrapped __init__ with @wraps before the NumpyDocstringInheritanceInitMeta metaclass ran, causing inspect.unwrap() to bypass the wrapper and read __doc__=None. This replaced every model’s description with the parent mixin’s and marked all model-specific parameters as “The description is missing” when DOCSTRING_INHERITANCE_ENABLE=1 was set during documentation builds (#971 by Bruno Aristimunha)

• Expose max_nbytes directly on braindecode.preprocessing.preprocess.preprocess() and turn the cryptic mmap can't resize a readonly failure (raised when joblib memory-maps a preloaded array that a preprocessor later tries to resize) into an actionable error explaining how to fix it: pass max_nbytes=None to disable memory mapping, or supply a save_dir so data is reloaded with preload=False (#325 by Sarthak Tayal)

Code health

• Reorder model categories in documentation to follow the progression: Convolution, Filterbank, Interpretability, Recurrent, Attention/Transformer, SPD, Graph Neural Network, Channel, and Foundation Model (#962 by Bruno Aristimunha)

• Fix documentation build warnings and errors: correct numpydoc section underlines in braindecode.models.EEGSym and braindecode.models.SSTDPN, strip upstream .. rubric:: directives from MNE and MOABB docstrings that caused Sphinx errors, fix RST title levels in whats_new.rst, correct bibtex key for EEGPT, and ensure conf.py prioritises the local package on sys.path (by Bruno Aristimunha)

• 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)

Authors