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Architecture-only repository. Documents thebraindecode.models.BIOTclass. No pretrained weights are distributed here. Instantiate the model and train it on your own data.
pip install braindecode1from braindecode.models import BIOT
2
3model = BIOT(
4 n_chans=16,
5 sfreq=200,
6 input_window_seconds=10.0,
7 n_outputs=2,
8)
| 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 |
1@article{aristimunha2025braindecode,
2 title = {Braindecode: a deep learning library for raw electrophysiological data},
3 author = {Aristimunha, Bruno and others},
4 journal = {Zenodo},
5 year = {2025},
6 doi = {10.5281/zenodo.17699192},
7}