README.md

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

# CodeBrain

CodeBrain: Scalable Code EEG Pre-Training for Unified Downstream BCI Tasks.

> **Architecture-only repository.** Documents the
> `braindecode.models.CodeBrain` 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 CodeBrain

model = CodeBrain(
    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.CodeBrain.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/codebrain.py#L21>


## Architecture

![CodeBrain architecture](https://raw.githubusercontent.com/jingyingma01/CodeBrain/refs/heads/main/assets/intro.png)


## Parameters

| Parameter | Type | Description |
|---|---|---|
| `patch_size` | int, default=200 | Number of time samples per patch. Input length is trimmed to the nearest multiple of `patch_size`. |
| `res_channels` | int, default=200 | Width of the residual stream inside each `ResidualBlock`. |
| `skip_channels` | int, default=200 | Width of the skip-connection stream aggregated across blocks. |
| `out_channels` | int, default=200 | Output channels of `final_conv` before the classification head. |
| `num_res_layers` | int, default=8 | Number of stacked `ResidualBlock` modules. |
| `drop_prob` | float, default=0.1 | Dropout rate used inside the `_GConv` SSM and attention layers. |
| `s4_bidirectional` | bool, default=True | Whether the `_GConv` SSM processes the sequence bidirectionally. |
| `s4_layernorm` | bool, default=False | Whether to apply layer normalisation inside the `_GConv` SSM. Set to `False` to match the released pretrained checkpoint. |
| `s4_lmax` | int, default=570 | Maximum sequence length for the `_GConv` SSM kernel. Also determines the patch embedding dimension as `s4_lmax // n_chans`. |
| `s4_d_state` | int, default=64 | State dimension of the `_GConv` SSM. |
| `conv_out_chans` | int, default=25 | Number of output channels in the patch projection convolutions. |
| `conv_groups` | int, default=5 | Number of groups for `GroupNorm` in the patch projection. |
| `activation` | type[nn.Module], default=nn.ReLU | Non-linear activation class used in `init_conv` and `final_conv`. |


## References

1. Yi Ding, Xuyang Chen, Yong Li, Rui Yan, Tao Wang, Le Wu (2025). CodeBrain: Scalable Code EEG Pre-Training for Unified Downstream BCI Tasks. https://arxiv.org/abs/2506.09110


## 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.