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Architecture-only repository. Documents thebraindecode.models.ShallowFBCSPNetclass. No pretrained weights are distributed here. Instantiate the model and train it on your own data.
pip install braindecode1from braindecode.models import ShallowFBCSPNet
2
3model = ShallowFBCSPNet(
4 n_chans=22,
5 sfreq=250,
6 input_window_seconds=4.0,
7 n_outputs=4,
8)
| Parameter | Type | Description |
|---|---|---|
n_filters_time: int | — | Number of temporal filters. |
filter_time_length: int | — | Length of the temporal filter. |
n_filters_spat: int | — | Number of spatial filters. |
pool_time_length: int | — | Length of temporal pooling filter. |
pool_time_stride: int | — | Length of stride between temporal pooling filters. |
| `final_conv_length: int | str` | — |
| `conv_nonlin: type[nn.Module] | Callable` | — |
pool_mode: str | — | Method to use on pooling layers. "max" or "mean". |
activation_pool_nonlin: type[nn.Module] | — | Non-linear module class to be used after pooling layers. |
split_first_layer: bool | — | Split first layer into temporal and spatial layers (True) or just use temporal (False). There would be no non-linearity between the split layers. |
batch_norm: bool | — | Whether to use batch normalisation. |
batch_norm_alpha: float | — | Momentum for BatchNorm2d. |
drop_prob: float | — | Dropout probability. |
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}