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

pip install braindecode

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

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:

@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.