| --- |
| 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. |
|
|
