braindecode/model-explorer

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:

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.