README.md

---
license: bsd-3-clause
library_name: braindecode
pipeline_tag: feature-extraction
tags:
  - eeg
  - biosignal
  - pytorch
  - neuroscience
  - braindecode
  - foundation-model
  - transformer
---

# CBraMod

**C**\ riss-\ **C**\ ross **Bra**\ in **Mod**\ el for EEG Decoding from Wang et al. (2025) [cbramod].

> **Architecture-only repository.** Documents the
> `braindecode.models.CBraMod` class. **No pretrained weights are
> distributed here.** Instantiate the model and train it on your own
> data.

## Quick start

```bash
pip install braindecode
```

```python
from braindecode.models import CBraMod

model = CBraMod(
    n_chans=22,
    sfreq=200,
    input_window_seconds=4.0,
    n_outputs=2,
)
```

The signal-shape arguments above are illustrative defaults — adjust to
match your recording.

## Documentation
- Full API reference: <https://braindecode.org/stable/generated/braindecode.models.CBraMod.html>
- Interactive browser (live instantiation, parameter counts):
  <https://huggingface.co/spaces/braindecode/model-explorer>
- Source on GitHub: <https://github.com/braindecode/braindecode/blob/master/braindecode/models/cbramod.py#L23>


## Architecture

![CBraMod architecture](https://raw.githubusercontent.com/wjq-learning/CBraMod/refs/heads/main/figure/model.png)


## Parameters

| Parameter | Type | Description |
|---|---|---|
| `patch_size` | int, default=200 | Temporal patch size in samples (200 samples = 1 second at 200 Hz). |
| `dim_feedforward` | int, default=800 | Dimension of the feedforward network in Transformer layers. |
| `n_layer` | int, default=12 | Number of Transformer layers. |
| `nhead` | int, default=8 | Number of attention heads. |
| `activation` | type[nn.Module], default=nn.GELU | Activation function used in Transformer feedforward layers. |
| `emb_dim` | int, default=200 | Output embedding dimension. |
| `drop_prob` | float, default=0.1 | Dropout probability. |
| `return_encoder_output` | bool, default=False | If false (default), the features are flattened and passed through a final linear layer to produce class logits of size `n_outputs`. If True, the model returns the encoder output features. |


## References

1. Wang, J., Zhao, S., Luo, Z., Zhou, Y., Jiang, H., Li, S., Li, T., & Pan, G. (2025). CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding. In The Thirteenth International Conference on Learning Representations (ICLR 2025). https://arxiv.org/abs/2412.07236


## Citation

Cite the original architecture paper (see *References* above) and braindecode:

```bibtex
@article{aristimunha2025braindecode,
  title   = {Braindecode: a deep learning library for raw electrophysiological data},
  author  = {Aristimunha, Bruno and others},
  journal = {Zenodo},
  year    = {2025},
  doi     = {10.5281/zenodo.17699192},
}
```

## License

BSD-3-Clause for the model code (matching braindecode).
Pretraining-derived weights, if you fine-tune from a checkpoint,
inherit the licence of that checkpoint and its training corpus.