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

pip install braindecode

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:

@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.