braindecode/brant-pretrained

Brant — Foundation Model for Intracranial Neural Signals

Pretrained weights for braindecode.models.Brant, a faithful braindecode port of Brant (Zhang et al., NeurIPS 2023), a foundation model for intracranial (sEEG/iEEG) recordings.

• Paper: Brant: Foundation Model for Intracranial Neural Signal (NeurIPS 2023)
• Original code & weights: yzz673/Brant · Daoze/Brant
• braindecode docs: https://braindecode.org

Provenance & license

These weights are the official pretrained checkpoint released by the original authors (temporal + spatial encoders), converted into the braindecode Brant module (state dict mapped 1:1; the two mask-token embeddings used only for the masked-autoencoding pretraining objective are dropped). The original release is under the Apache-2.0 license, which this repository preserves.

Disclaimer (from the original authors). The pre-training data for Brant was collected during routine treatment of epilepsy patients and is intended solely for medical or research use. These pre-trained weights are released only for medical or research purposes and must not be subjected to any form of misuse.

Model configuration

This checkpoint uses the paper's configuration (~508M parameters):

patch_size	1500 (6 s at 250 Hz)
embed_dim	2048
ffn_dim	3072
temporal_n_layers	12
spatial_n_layers	5
n_heads	16
n_freq_bands	8
n_times	22500 (15 patches, 90 s at 250 Hz)
sfreq	250 Hz

The signal is expected at 250 Hz. The learnable temporal positional encoding is fixed to 15 patches, so keep n_times=22500; you may freely change n_chans (channels are pooled) and n_outputs (the classification head is task-specific and randomly initialized — fine-tune it on your downstream task).

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
private:Brant-SEEG	Brant private SEEG corpus collected by the authors during routine epilepsy treatment (not released)	pretraining	iEEG

• masked-autoencoding pretraining on the authors' private clinical SEEG corpus: 9 subjects, 2,528 h at 1000 Hz (1.01 TB), one hospital (unnamed); the separate 43 h private seizure set is downstream only and not in this checkpoint
• Sources: https://github.com/yzz673/Brant README; Zhang et al., NeurIPS 2023 (proceedings PDF via web.archive.org) p.4, Section 3.1.

Usage

from braindecode.models import Brant

# Encoders load the pretrained weights; the classification head is (re)initialized
# for your task via n_outputs.
model = Brant.from_pretrained("braindecode/brant-pretrained", n_outputs=2)

Citation

@inproceedings{zhang2023brant,
  title     = {Brant: Foundation Model for Intracranial Neural Signal},
  author    = {Zhang, Daoze and Yuan, Zhizhang and Yang, Yang and Chen, Junru and Wang, Jingjing and Li, Yafeng},
  booktitle = {Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS)},
  year      = {2023}
}

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},
}