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