BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG data
Paper • 2101.12037 • Published
This model has been pushed to the Hub using the PytorchModelHubMixin integration:
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 |
|---|---|---|---|
TUEG |
Temple University Hospital EEG Corpus (TUH EEG) | pretraining | EEG |
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.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},
}