SyncNet

Synchronization Network (SyncNet) from Li, Y et al (2017) [Li2017].

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

model = SyncNet(
    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.SyncNet.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/syncnet.py#L14

Architecture

Parameters

Parameter	Type	Description
num_filters	int, optional	Number of filters in the convolutional layer. Default is 1.
filter_width	int, optional	Width of the convolutional filters. Default is 40.
pool_size	int, optional	Size of the pooling window. Default is 40.
activation	nn.Module, optional	Activation function to apply after pooling. Default is nn.ReLU.
ampli_init_values	tuple of float, optional	The initialization range for amplitude parameter using uniform distribution. Default is (-0.05, 0.05).
omega_init_values	tuple of float, optional	The initialization range for omega parameters using uniform distribution. Default is (0, 1).
beta_init_values	tuple of float, optional	The initialization range for beta (decay) parameters using uniform distribution. Default is (0, 0.05).
phase_init_values	tuple of float, optional	The initialization mean and standard deviation for phase parameters using normal distribution. Default is (0, 0.05).

References

1. Li, Y., Dzirasa, K., Carin, L., & Carlson, D. E. (2017). Targeting EEG/LFP synchrony with neural nets. Advances in neural information processing systems, 30.
2. Code from Baselines for EEG-Music Emotion Recognition Grand Challenge at ICASSP 2025. https://github.com/SalvoCalcagno/eeg-music-challenge-icassp-2025-baselines

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.