File size: 3,005 Bytes
d982eaa 43f0c51 d982eaa 43f0c51 d982eaa 43f0c51 d982eaa 43f0c51 d982eaa 43f0c51 d982eaa 43f0c51 d982eaa 43f0c51 d982eaa | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 | ---
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
## 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.
|