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

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 (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	BCI Competition IV dataset 2a (4-class motor imagery, 9 subjects, 22 EEG channels), MOABB BNCI2014_001	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, and the original model and datasets cited above.

@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},
}