| --- |
| license: bsd-3-clause |
| library_name: braindecode |
| pipeline_tag: feature-extraction |
| tags: |
| - eeg |
| - biosignal |
| - pytorch |
| - neuroscience |
| - braindecode |
| - convolutional |
| --- |
| |
| # SincShallowNet |
|
|
| Sinc-ShallowNet from Borra, D et al (2020) [borra2020]. |
|
|
| > **Architecture-only repository.** Documents the |
| > `braindecode.models.SincShallowNet` 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 SincShallowNet |
| |
| model = SincShallowNet( |
| 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.SincShallowNet.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/sinc_shallow.py#L11> |
|
|
|
|
| ## Architecture |
|
|
|  |
|
|
|
|
| ## Parameters |
|
|
| | Parameter | Type | Description | |
| |---|---|---| |
| | `num_time_filters` | int | Number of temporal filters in the SincFilter layer. | |
| | `time_filter_len` | int | Size of the temporal filters. | |
| | `depth_multiplier` | int | Depth multiplier for spatial filtering. | |
| | `activation` | nn.Module, optional | Activation function to use. Default is nn.ELU(). | |
| | `drop_prob` | float, optional | Dropout probability. Default is 0.5. | |
| | `first_freq` | float, optional | The starting frequency for the first Sinc filter. Default is 5.0. | |
| | `min_freq` | float, optional | Minimum frequency allowed for the low frequencies of the filters. Default is 1.0. | |
| | `freq_stride` | float, optional | Frequency stride for the Sinc filters. Controls the spacing between the filter frequencies. Default is 1.0. | |
| | `padding` | str, optional | Padding mode for convolution, either 'same' or 'valid'. Default is 'same'. | |
| | `bandwidth` | float, optional | Initial bandwidth for each Sinc filter. Default is 4.0. | |
| | `pool_size` | int, optional | Size of the pooling window for the average pooling layer. Default is 55. | |
| | `pool_stride` | int, optional | Stride of the pooling operation. Default is 12. | |
|
|
|
|
| ## References |
|
|
| 1. Borra, D., Fantozzi, S., & Magosso, E. (2020). Interpretable and lightweight convolutional neural network for EEG decoding: Application to movement execution and imagination. Neural Networks, 129, 55-74. |
| 2. Sinc-ShallowNet re-implementation source code: https://github.com/marcellosicbaldi/SincNet-Tensorflow |
|
|
|
|
| ## 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. |
|
|
