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license: bsd-3-clause
library_name: braindecode
pipeline_tag: feature-extraction
tags:
- eeg
- biosignal
- pytorch
- neuroscience
- braindecode
- convolutional
---
# EEGSimpleConv
EEGSimpleConv from Ouahidi, YE et al (2023) [Yassine2023].
> **Architecture-only repository.** Documents the
> `braindecode.models.EEGSimpleConv` 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 EEGSimpleConv
model = EEGSimpleConv(
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.EEGSimpleConv.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/eegsimpleconv.py#L21>
## Architecture
## Parameters
| Parameter | Type | Description |
|---|---|---|
| `feature_maps: int` | — | Number of Feature Maps at the first Convolution, width of the model. |
| `n_convs: int` | — | Number of blocks of convolutions (2 convolutions per block), depth of the model. |
| `resampling: int` | — | Resampling Frequency. |
| `kernel_size: int` | — | Size of the convolutions kernels. |
| `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
1. Yassine El Ouahidi, V. Gripon, B. Pasdeloup, G. Bouallegue N. Farrugia, G. Lioi, 2023. A Strong and Simple Deep Learning Baseline for BCI Motor Imagery Decoding. Arxiv preprint. arxiv.org/abs/2309.07159
2. Yassine El Ouahidi, V. Gripon, B. Pasdeloup, G. Bouallegue N. Farrugia, G. Lioi, 2023. A Strong and Simple Deep Learning Baseline for BCI Motor Imagery Decoding. GitHub repository. https://github.com/elouayas/EEGSimpleConv.
## 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.
|