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.** This repo documents the
> `braindecode.models.CodeBrain` class. **No pretrained weights are
> distributed here** — instantiate the model and train it on your own
> data, or fine-tune from a published foundation-model checkpoint
> separately.

## 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 example defaults — adjust them
to match your recording.

## Documentation

- Full API reference (parameters, references, architecture figure):
  <https://braindecode.org/stable/generated/braindecode.models.CodeBrain.html>
- Interactive browser with live instantiation:
  <https://huggingface.co/spaces/braindecode/model-explorer>
- Source on GitHub: <https://github.com/braindecode/braindecode/blob/master/braindecode/models/codebrain.py#L21>

## Architecture description

The block below is the rendered class docstring (parameters,
references, architecture figure where available).

<div class='bd-doc'><main>
<p>CodeBrain: Scalable Code EEG Pre-Training for Unified Downstream BCI Tasks.</p>
<span style="display:inline-block;padding:2px 8px;border-radius:4px;background:#d9534f;color:white;font-size:11px;font-weight:600;margin-right:4px;">Foundation Model</span><span style="display:inline-block;padding:2px 8px;border-radius:4px;background:#56B4E9;color:white;font-size:11px;font-weight:600;margin-right:4px;">Attention/Transformer</span>



 .. figure:: https://raw.githubusercontent.com/jingyingma01/CodeBrain/refs/heads/main/assets/intro.png
     :align: center
     :alt: CodeBrain pre-training overview
     :width: 1000px

 CodeBrain is a foundation model for EEG that pre-trains on large unlabelled
 corpora using a two-stage vector-quantised masking strategy, then fine-tunes
 on downstream BCI tasks. It segments EEG signals into fixed-size patches,
 embeds them with convolutional and spectral projections, and processes them
 through stacked residual blocks that combine a multi-scale convolutional
 structured state-space model (``_GConv``) with sliding-window self-attention.

 .. rubric:: Stage 2: EEGSSM Backbone (this implementation)

 This class implements Stage 2 of CodeBrain — the EEGSSM backbone described
 in Section 3.3 of [codebrain]_. Following :class:`Labram`, CodeBrain
 discretises EEG patches into codebook tokens via VQ-VAE (Stage 1, not
 implemented here), then trains the backbone to predict masked token indices
 via cross-entropy. CodeBrain extends this with a *dual* tokenizer that
 decouples temporal and frequency representations, as stated in the paper:
 *"the TFDual-Tokenizer, which decouples heterogeneous temporal and frequency
 EEG signals into discrete tokens to enhance discriminative power."*

 .. rubric:: Macro Components

 - **PatchEmbedding**: Splits ``(batch, n_chans, n_times)`` into
   ``(batch, n_chans, seq_len, patch_size)`` patches, projects each patch
   with a 2-D convolutional stack, adds FFT-based spectral embeddings, and
   applies depth-wise convolutional positional encoding.
 - **Residual blocks** (``ResidualGroup``): Each block applies RMSNorm,
   a ``_GConv`` SSM layer, and sliding-window multi-head attention, with
   gated activation and separate residual/skip paths.
 - **Classification head** (``final_layer``): Flattens the output and maps
   to ``n_outputs`` classes.

 .. important::
    **Pre-trained Weights Available**

    This model has pre-trained weights available on the Hugging Face Hub.
    You can load them using:

    .. code:: python
        from braindecode.models import CodeBrain

        # Load pre-trained model from Hugging Face Hub
        model = CodeBrain.from_pretrained("braindecode/codebrain-pretrained")

    To push your own trained model to the Hub:

    .. code:: python
        model.push_to_hub("my-username/my-codebrain")

 Parameters
 ----------
 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
 ----------
 .. [codebrain] 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

 .. rubric:: Hugging Face Hub integration

 When the optional ``huggingface_hub`` package is installed, all models
 automatically gain the ability to be pushed to and loaded from the
 Hugging Face Hub. Install with::

     pip install braindecode[hub]

 **Pushing a model to the Hub:**

 .. code::
     from braindecode.models import CodeBrain

     # Train your model
     model = CodeBrain(n_chans=22, n_outputs=4, n_times=1000)
     # ... training code ...

     # Push to the Hub
     model.push_to_hub(
         repo_id="username/my-codebrain-model",
         commit_message="Initial model upload",
     )

 **Loading a model from the Hub:**

 .. code::
     from braindecode.models import CodeBrain

     # Load pretrained model
     model = CodeBrain.from_pretrained("username/my-codebrain-model")

     # Load with a different number of outputs (head is rebuilt automatically)
     model = CodeBrain.from_pretrained("username/my-codebrain-model", n_outputs=4)

 **Extracting features and replacing the head:**

 .. code::
     import torch

     x = torch.randn(1, model.n_chans, model.n_times)
     # Extract encoder features (consistent dict across all models)
     out = model(x, return_features=True)
     features = out["features"]

     # Replace the classification head
     model.reset_head(n_outputs=10)

 **Saving and restoring full configuration:**

 .. code::
     import json

     config = model.get_config()            # all __init__ params
     with open("config.json", "w") as f:
         json.dump(config, f)

     model2 = CodeBrain.from_config(config)    # reconstruct (no weights)

 All model parameters (both EEG-specific and model-specific such as
 dropout rates, activation functions, number of filters) are automatically
 saved to the Hub and restored when loading.

 See :ref:`load-pretrained-models` for a complete tutorial.</main>
</div>

## Citation

Please cite both the original paper for this architecture (see the
*References* section 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.