| --- |
| library_name: braindecode |
| license: apache-2.0 |
| tags: |
| - Brant |
| - braindecode |
| - ieeg |
| - seeg |
| - foundation-model |
| - model_hub_mixin |
| - pytorch_model_hub_mixin |
| --- |
| |
| # Brant — Foundation Model for Intracranial Neural Signals |
|
|
| Pretrained weights for [`braindecode.models.Brant`](https://braindecode.org/stable/generated/braindecode.models.Brant.html), |
| 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](https://proceedings.neurips.cc/paper_files/paper/2023/hash/535915d26859036410b0533804cee788-Abstract-Conference.html) (NeurIPS 2023) |
| - Original code & weights: [yzz673/Brant](https://github.com/yzz673/Brant) · [Daoze/Brant](https://huggingface.co/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). |
|
|
| ## Usage |
|
|
| ```python |
| 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 |
|
|
| ```bibtex |
| @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} |
| } |
| ``` |
|
|
