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braindecode/BIOT

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BIOT

BIOT from Yang et al (2023) [Yang2023]

Architecture-only repository. Documents the braindecode.models.BIOT 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 BIOT

model = BIOT(
    n_chans=16,
    sfreq=200,
    input_window_seconds=10.0,
    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.BIOT.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/biot.py#L56>

Architecture

BIOT architecture

Parameters

Parameter	Type	Description
`embed_dim`	int, optional	The size of the embedding layer, by default 256
`num_heads`	int, optional	The number of attention heads, by default 8
`num_layers`	int, optional	The number of transformer layers, by default 4
`activation: nn.Module, default=nn.ELU`	—	Activation function class to apply. Should be a PyTorch activation module class like `nn.ReLU` or `nn.ELU`. Default is `nn.ELU`.
`return_feature: bool, optional`	—	Changing the output for the neural network. Default is single tensor when return_feature is True, return embedding space too. Default is False.
`hop_length: int, optional`	—	The hop length for the torch.stft transformation in the encoder. The default is 100.
`sfreq: int, optional`	—	The sfreq parameter for the encoder. The default is 200

References

1.Yang, C., Westover, M.B. and Sun, J., 2023, November. BIOT: Biosignal Transformer for Cross-data Learning in the Wild. In Thirty-seventh Conference on Neural Information Processing Systems, NeurIPS.
2.Yang, C., Westover, M.B. and Sun, J., 2023. BIOT Biosignal Transformer for Cross-data Learning in the Wild. GitHub https://github.com/ycq091044/BIOT (accessed 2024-02-13)

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.