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

pip install braindecode

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

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.
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:

@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.

Downloads last month: -; Downloads are not tracked for this model. How to track