README.md

metadata

title: Braindecode Model Explorer
emoji: 🧠
colorFrom: blue
colorTo: indigo
sdk: gradio
sdk_version: 5.0.0
python_version: '3.12'
app_file: app.py
pinned: false
license: bsd-3-clause
short_description: Browse 57 EEG / biosignal architectures from braindecode
tags:
  - eeg
  - meg
  - ecog
  - biosignal
  - pytorch
  - neuroscience
  - brain-computer-interface
  - deep-learning

Braindecode Model Explorer

Interactive browser for 57 EEG / biosignal model architectures from braindecode.

For each model you can:

• read the rendered docstring (architecture figure, parameters, references);
• configure the input signal shape (n_chans, sfreq, input_window_seconds, n_outputs);
• instantiate the model live and inspect parameter count, layer summary (via torchinfo), and output shape on a dummy forward pass.

No pretrained weights are loaded — this Space is a pure architecture explorer, runs on the free CPU tier, and never downloads checkpoints. For curated foundation-model weights, see huggingface.co/braindecode.

Models included

All classes that subclass braindecode.models.base.EEGModuleMixin, auto-discovered at startup. Examples by family:

Family	Examples
Foundation models	BIOT, BENDR, SignalJEPA, Labram, EEGPT, CodeBrain, LUNA
Convolutional	EEGNet, Deep4Net, ShallowFBCSPNet, EEGITNet, EEGNeX
Transformer	EEGConformer, ATCNet, MSVTNet, MEDFormer, CTNet
Sleep staging	USleep, SleepStagerChambon2018, AttnSleep, DeepSleepNet
Filter-bank	FBCNet, FBLightConvNet, FBMSNet, IFNet
Other	DGCNN, TSception, SyncNet, REVE, SCCNet

Local development

pip install -r requirements.txt
python app.py

Open http://localhost:7860.

How docstrings are rendered

Braindecode docstrings use NumpyDoc + Sphinx extensions (.. figure::, :bdg-danger:, .. versionadded::). The docstring_renderer module maps Sphinx-only directives to plain rST, then renders to HTML via docutils. No Sphinx build is needed at runtime — the Space stays dependency-light and rebuilds in seconds.

Citation

@article{HBM:HBM23730,
  author  = {Schirrmeister, Robin Tibor and Springenberg, Jost Tobias
             and Fiederer, Lukas Dominique Josef and Glasstetter, Martin
             and Eggensperger, Katharina and Tangermann, Michael and Hutter,
             Frank and Burgard, Wolfram and Ball, Tonio},
  title   = {Deep learning with convolutional neural networks for EEG
             decoding and visualization},
  journal = {Human Brain Mapping},
  year    = {2017},
  doi     = {10.1002/hbm.23730},
}

License

BSD-3-Clause, matching the upstream braindecode library.