| --- |
| library_name: braindecode |
| license: bsd-3-clause |
| tags: |
| - CodeBrain |
| - braindecode |
| - model_hub_mixin |
| - pytorch_model_hub_mixin |
| datasets: |
| - TUEG |
| --- |
| |
| This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: |
| - Code: https://braindecode.org |
| - Paper: [More Information Needed] |
| - Docs: https://braindecode.org/stable/generated/braindecode.models.CodeBrain.html |
|
|
| <!-- braindecode:training-data --> |
| ## Training data |
|
|
| The data that shaped these weights. The ids are the `datasets:` field of this card, |
| which braindecode reads into `model.card_data.datasets` when you call |
| `from_pretrained` (MOABB class names for MOABB datasets, OpenNeuro / NEMAR |
| accessions, Hub ids otherwise). `nm`/`on` ids are datasets on [NEMAR](https://nemar.org) |
| (`on` = NEMAR's copy of the OpenNeuro dataset with the same number). Rows marked |
| `private:` are data the authors did not release; they are not in `datasets:`. |
|
|
| | `datasets:` id | Dataset | Stage | Modality | |
| |---|---|---|---| |
| | `TUEG` | [Temple University Hospital EEG Corpus (TUH EEG)](https://isip.piconepress.com/projects/nedc/html/tuh_eeg/) | pretraining | EEG | |
|
|
| - stage 1 TFDual-Tokenizer (20 epochs) and stage 2 EEGSSM masked code prediction (10 epochs), both on TUEG; 19 ch, 200 Hz, 1,109,545 samples (~9,246 h); TUAB/TUEV not removed in the main model |
| - `TUEG` (the TUH EEG Corpus) contains the recordings of its sub-corpora TUAB, TUEV, TUSZ, TUAR, TUSL and TUEP; an evaluation on one of those is not held out from these weights unless the authors removed it. |
| - Sources: arXiv:2506.09110 Sec. 3.1 (Datasets, Pre-Training); arXiv:2506.09110 Appendix (TUAB/TUEV results); braindecode/models/codebrain.py (W/bd) _remap docstring; HF commit braindecode/codebrain-pretrained 2026-04-03. |
| |
| |
| |
| ## Pretrained components |
| |
| Only the tokenizer/encoder weights are pretrained. The task classification head in this braindecode export is newly initialized; fine-tune it for your downstream task. |
| |
| |
| ## Cite Braindecode |
| |
| If you use this braindecode port in scientific work, cite the software on Zenodo, |
| [doi:10.5281/zenodo.17699192](https://doi.org/10.5281/zenodo.17699192), |
| and the original model and datasets cited above. |
| |
| ```bibtex |
| @software{braindecode, |
| author = {Aristimunha, Bruno and |
| Guetschel, Pierre and |
| Wimpff, Martin and |
| Gemein, Lukas and |
| Rommel, Cedric and |
| Banville, Hubert and |
| Sliwowski, Maciej and |
| Wilson, Daniel and |
| Brandt, Simon and |
| Gnassounou, Théo and |
| Paillard, Joseph and |
| {Junqueira Lopes}, Bruna and |
| Sedlar, Sara and |
| Moreau, Thomas and |
| Chevallier, Sylvain and |
| Gramfort, Alexandre and |
| Schirrmeister, Robin Tibor}, |
| title = {Braindecode: toolbox for decoding raw electrophysiological brain data |
| with deep learning models}, |
| url = {https://github.com/braindecode/braindecode}, |
| doi = {10.5281/zenodo.17699192}, |
| publisher = {Zenodo}, |
| license = {BSD-3-Clause}, |
| } |
| ``` |
| |
