Add model card (provenance, license, config, usage)

README.md

@@ -1,14 +1,77 @@
 ---
 library_name: braindecode
-license: bsd-3-clause
+license: apache-2.0
 tags:
 - Brant
 - braindecode
+- ieeg
+- seeg
+- foundation-model
 - model_hub_mixin
 - pytorch_model_hub_mixin
 ---
 
-This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
-- Code: https://braindecode.org
-- Paper: [More Information Needed]
-- Docs: https://braindecode.org/stable/generated/braindecode.models.Brant.html
+# 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}
+}
+```