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

pip install braindecode

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

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:

@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.