SCCNet

SCCNet from Wei, C S (2019) [sccnet].

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

model = SCCNet(
    n_chans=22,
    sfreq=250,
    input_window_seconds=4.0,
    n_outputs=4,
)

The signal-shape arguments above are illustrative defaults — adjust to match your recording.

Documentation

Full API reference: https://braindecode.org/stable/generated/braindecode.models.SCCNet.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/sccnet.py#L17

Architecture

Parameters

Parameter	Type	Description
`n_spatial_filters`	int, optional	Number of spatial filters in the first convolutional layer, variable `N_u` from the original paper. Default is 22.
`n_spatial_filters_smooth`	int, optional	Number of spatial filters used as filter in the second convolutional layer. Default is 20.
`drop_prob`	float, optional	Dropout probability. Default is 0.5.
`activation`	nn.Module, optional	Activation function after the second convolutional layer. Default is logarithm activation.

References

Wei, C. S., Koike-Akino, T., & Wang, Y. (2019, March). Spatial component-wise convolutional network (SCCNet) for motor-imagery EEG classification. In 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER) (pp. 328-331). IEEE.
Hsieh, C. Y., Chou, J. L., Chang, Y. H., & Wei, C. S. XBrainLab: An Open-Source Software for Explainable Artificial Intelligence-Based EEG Analysis. In NeurIPS 2023 AI for Science Workshop.

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.

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