File size: 4,012 Bytes
6ff1e26 c6a7336 6ff1e26 c6a7336 6ff1e26 c6a7336 6ff1e26 c6a7336 6ff1e26 c6a7336 6ff1e26 c6a7336 6ff1e26 c6a7336 6ff1e26 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 | ---
license: bsd-3-clause
library_name: braindecode
pipeline_tag: feature-extraction
tags:
- eeg
- biosignal
- pytorch
- neuroscience
- braindecode
- convolutional
---
# EEGNet
EEGNet model from Lawhern et al (2018) [Lawhern2018].
> **Architecture-only repository.** Documents the
> `braindecode.models.EEGNet` 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 EEGNet
model = EEGNet(
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.EEGNet.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/eegnet.py#L22>
## Architecture
## Parameters
| Parameter | Type | Description |
|---|---|---|
| `final_conv_length` | int or "auto", default="auto" | Length of the final convolution layer. If "auto", it is set based on n_times. |
| `pool_mode` | {"mean", "max"}, default="mean" | Pooling method to use in pooling layers. |
| `F1` | int, default=8 | Number of temporal filters in the first convolutional layer. |
| `D` | int, default=2 | Depth multiplier for the depthwise convolution. |
| `F2` | int or None, default=None | Number of pointwise filters in the separable convolution. Usually set to `F1 * D`. |
| `depthwise_kernel_length` | int, default=16 | Length of the depthwise convolution kernel in the separable convolution. |
| `pool1_kernel_size` | int, default=4 | Kernel size of the first pooling layer. |
| `pool2_kernel_size` | int, default=8 | Kernel size of the second pooling layer. |
| `kernel_length` | int, default=64 | Length of the temporal convolution kernel. |
| `conv_spatial_max_norm` | float, default=1 | Maximum norm constraint for the spatial (depthwise) convolution. |
| `activation` | nn.Module, default=nn.ELU | Non-linear activation function to be used in the layers. |
| `batch_norm_momentum` | float, default=0.01 | Momentum for instance normalization in batch norm layers. |
| `batch_norm_affine` | bool, default=True | If True, batch norm has learnable affine parameters. |
| `batch_norm_eps` | float, default=1e-3 | Epsilon for numeric stability in batch norm layers. |
| `drop_prob` | float, default=0.25 | Dropout probability. |
| `final_layer_with_constraint` | bool, default=False | If `False`, uses a convolution-based classification layer. If `True`, apply a flattened linear layer with constraint on the weights norm as the final classification step. |
| `norm_rate` | float, default=0.25 | Max-norm constraint value for the linear layer (used if `final_layer_conv=False`). |
## References
1. Lawhern, V. J., Solon, A. J., Waytowich, N. R., Gordon, S. M., Hung, C. P., & Lance, B. J. (2018). EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces. Journal of neural engineering, 15(5), 056013.
2. Chollet, F., *Xception: Deep Learning with Depthwise Separable Convolutions*, CVPR, 2017.
## 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.
|