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license: bsd-3-clause
library_name: braindecode
pipeline_tag: feature-extraction
tags:
- eeg
- biosignal
- pytorch
- neuroscience
- braindecode
- convolutional
---
# EEGSym
EEGSym from Pérez-Velasco et al (2022) [eegsym2022].
> **Architecture-only repository.** Documents the
> `braindecode.models.EEGSym` 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 EEGSym
model = EEGSym(
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.EEGSym.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/eegsym.py#L16>
## Architecture
## Parameters
| Parameter | Type | Description |
|---|---|---|
| `filters_per_branch` | int, optional | Number of filters in each inception branch. Should be a multiple of 8. Default is 12 [eegsym2022]. |
| `scales_time` | tuple of int, optional | Temporal scales (in milliseconds) for the temporal convolutions in the first inception module. Default is (500, 250, 125) [eegsym2022]. |
| `drop_prob` | float, optional | Dropout probability. Default is 0.25 [eegsym2022]. |
| `activation` | type[nn.Module], optional | Activation function class to use. Default is :class:`nn.ELU` [eegsym2022]. |
| `spatial_resnet_repetitions` | int, optional | Number of repetitions of the spatial analysis operations at each step. Default is 5 [eegsym2022]. |
| `left_right_chs` | list of tuple of str, optional | List of tuples pairing left and right hemisphere channel names, e.g., `[('C3', 'C4'), ('FC5', 'FC6')]`. If not provided, channels are automatically split into left/right hemispheres using :func:`~braindecode.datautil.channel_utils.division_channels_idx` and :func:`~braindecode.datautil.channel_utils.match_hemisphere_chans`. Must be provided together with `middle_chs` [eegsym2022]. |
| `middle_chs` | list of str, optional | List of midline (central) channel names that lie on the mid-sagittal plane, e.g., `['FZ', 'CZ', 'PZ']`. These channels are duplicated and concatenated to both hemispheres. If not provided, channels are automatically identified using :func:`~braindecode.datautil.channel_utils.division_channels_idx`. Must be provided together with `left_right_chs` [eegsym2022]. |
## References
1. Pérez-Velasco, S., Santamaría-Vázquez, E., Martínez-Cagigal, V., Marcos-Martínez, D., & Hornero, R. (2022). EEGSym: Overcoming inter-subject variability in motor imagery based BCIs with deep learning. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 30, 1766-1775.
2. Pérez-Velasco, S., EEGSym source code. https://github.com/Serpeve/EEGSym
3. Santamaría-Vázquez, E., Martínez-Cagigal, V., Vaquerizo-Villar, F., & Hornero, R. (2020). EEG-Inception: A novel deep convolutional neural network for assistive ERP-based brain-computer interfaces. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28, 2773-2782.
## 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.
|