| --- |
| license: bsd-3-clause |
| library_name: braindecode |
| pipeline_tag: feature-extraction |
| tags: |
| - eeg |
| - biosignal |
| - pytorch |
| - neuroscience |
| - braindecode |
| - convolutional |
| --- |
| |
| # SPARCNet |
|
|
| Seizures, Periodic and Rhythmic pattern Continuum Neural Network (SPaRCNet) from Jing et al (2023) [jing2023]. |
|
|
| > **Architecture-only repository.** Documents the |
| > `braindecode.models.SPARCNet` 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 SPARCNet |
| |
| model = SPARCNet( |
| 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.SPARCNet.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/sparcnet.py#L13> |
|
|
|
|
|
|
| ## Parameters |
|
|
| | Parameter | Type | Description | |
| |---|---|---| |
| | `block_layers` | int, optional | Number of layers per dense block. Default is 4. | |
| | `growth_rate` | int, optional | Growth rate of the DenseNet. Default is 16. | |
| | `bn_size` | int, optional | Bottleneck size. Default is 16. | |
| | `drop_prob` | float, optional | Dropout rate. Default is 0.5. | |
| | `conv_bias` | bool, optional | Whether to use bias in convolutional layers. Default is True. | |
| | `batch_norm` | bool, optional | Whether to use batch normalization. Default is True. | |
| | `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`. | |
|
|
|
|
| ## References |
|
|
| 1. Jing, J., Ge, W., Hong, S., Fernandes, M. B., Lin, Z., Yang, C., ... & Westover, M. B. (2023). Development of expert-level classification of seizures and rhythmic and periodic patterns during eeg interpretation. Neurology, 100(17), e1750-e1762. |
| 2. Yang, C., Westover, M.B. and Sun, J., 2023. BIOT Biosignal Transformer for Cross-data Learning in the Wild. GitHub https://github.com/ycq091044/BIOT (accessed 2024-02-13) |
|
|
|
|
| ## 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. |
|
|
