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Architecture-only repository. Documents thebraindecode.models.ContraWRclass. No pretrained weights are distributed here. Instantiate the model and train it on your own data.
pip install braindecode1from braindecode.models import ContraWR
2
3model = ContraWR(
4 n_chans=22,
5 sfreq=250,
6 input_window_seconds=4.0,
7 n_outputs=4,
8)| Parameter | Type | Description |
|---|---|---|
steps | int, optional | Number of steps to take the frequency decomposition hop_length parameters by default 20. |
emb_size | int, optional | Embedding size for the final layer, by default 256. |
res_channels | list[int], optional | Number of channels for each residual block, by default [32, 64, 128]. |
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. |
drop_prob | float, default=0.5 | The dropout rate for regularization. Values should be between 0 and 1. |
.. versionadded:: 0.9 | — | — |
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}