| --- |
| license: bsd-3-clause |
| library_name: braindecode |
| pipeline_tag: feature-extraction |
| tags: |
| - eeg |
| - biosignal |
| - pytorch |
| - neuroscience |
| - braindecode |
| - convolutional |
| --- |
| |
| # 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 |
|
|
| ```bash |
| pip install braindecode |
| ``` |
|
|
| ```python |
| 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 |
|
|
| 1. 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. |
| 2. 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: |
|
|
| ```bibtex |
| @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. |
|
|
