| --- |
| datasets: |
| - nm000139 |
| --- |
| |
| # plot_bcic_iv_2a_eegprep_cleaning |
| |
| Pretrained artifacts for the Braindecode tutorial `examples/model_building/plot_bcic_iv_2a_eegprep_cleaning.py`. |
| |
| These files are meant to be loaded by the tutorial so the docs can show stable predictions without retraining the model from scratch. |
| |
| ## Stored files |
| |
| - `params.safetensors`: classifier parameters |
| - `history.json`: Skorch training history used by the tutorial plots |
| - `metadata.json`: summary metrics for the stored checkpoint |
| |
| <!-- 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 | |
| |---|---|---|---| |
| | [`nm000139`](https://nemar.org/dataset/nm000139) | [BCI Competition IV dataset 2a (4-class motor imagery, 9 subjects, 22 EEG channels), MOABB BNCI2014_001](https://moabb.neurotechx.com/docs/generated/moabb.datasets.BNCI2014_001.html) | training | EEG | |
|
|
| - Supervised trialwise EEGNeX after EEGPrep cleaning, subject 3 only. Gradient updates on session '0train' (MOABB session name); session '1test' is the skorch validation set and EarlyStopping(load_best=True) restores the epoch with the best validation loss, so the evaluation session selected the saved epoch (no gradient updates on it). Up to 100 epochs, patience 10. Preprocessing adds EEGPrep(resample_to=128, bad_window_max_bad_channels=None) before the 4-38 Hz filter. |
| - Sources: braindecode git 05e606dd^:scripts/train_and_push_tutorial_checkpoints.py (offline trainer that produced the tutorial checkpoints; removed from the repo in 05e606dd, 2026-04-19, and moved to https://gist.github.com/bru...; https://huggingface.co/braindecode/plot_bcic_iv_2a_eegprep_cleaning/resolve/main/metadata.json; W/bd/examples/model_building/plot_bcic_iv_2a_eegprep_cleaning.py (gallery example of the same name). |
| |
| |
| |
| ## 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}, |
| } |
| ``` |
| |
