README.md

---
license: bsd-3-clause
library_name: braindecode
pipeline_tag: feature-extraction
tags:
  - eeg
  - biosignal
  - pytorch
  - neuroscience
  - braindecode
  - convolutional
---

# EEGNeX

EEGNeX model from Chen et al (2024) [eegnex].

> **Architecture-only repository.** Documents the
> `braindecode.models.EEGNeX` 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 EEGNeX

model = EEGNeX(
    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.EEGNeX.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/eegnex.py#L16>


## Architecture

![EEGNeX architecture](https://braindecode.org/dev/_static/model/eegnex.jpg)


## Parameters

| Parameter | Type | Description |
|---|---|---|
| `activation` | nn.Module, optional | Activation function to use. Default is `nn.ELU`. |
| `depth_multiplier` | int, optional | Depth multiplier for the depthwise convolution. Default is 2. |
| `filter_1` | int, optional | Number of filters in the first convolutional layer. Default is 8. |
| `filter_2` | int, optional | Number of filters in the second convolutional layer. Default is 32. |
| `drop_prob: float, optional` | — | Dropout rate. Default is 0.5. |
| `kernel_block_4` | tuple[int, int], optional | Kernel size for block 4. Default is (1, 16). |
| `dilation_block_4` | tuple[int, int], optional | Dilation rate for block 4. Default is (1, 2). |
| `avg_pool_block4` | tuple[int, int], optional | Pooling size for block 4. Default is (1, 4). |
| `kernel_block_5` | tuple[int, int], optional | Kernel size for block 5. Default is (1, 16). |
| `dilation_block_5` | tuple[int, int], optional | Dilation rate for block 5. Default is (1, 4). |
| `avg_pool_block5` | tuple[int, int], optional | Pooling size for block 5. Default is (1, 8). |


## References

1. Chen, X., Teng, X., Chen, H., Pan, Y., & Geyer, P. (2024). Toward reliable signals decoding for electroencephalogram: A benchmark study to EEGNeX. Biomedical Signal Processing and Control, 87, 105475.
2. Chen, X., Teng, X., Chen, H., Pan, Y., & Geyer, P. (2024). Toward reliable signals decoding for electroencephalogram: A benchmark study to EEGNeX. https://github.com/chenxiachan/EEGNeX


## 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.