---
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`](https://braindecode.org).

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`](https://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

```bash
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

```bibtex
@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.