README.md

---
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

![EEGSimpleConv architecture](https://raw.githubusercontent.com/elouayas/EEGSimpleConv/refs/heads/main/architecture.png)


## 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.