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):

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).

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}
}