README.md

metadata

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

EEGNetv4

Deprecated alias for EEGNet.

Architecture-only repository. Documents the braindecode.models.EEGNetv4 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 EEGNetv4

model = EEGNetv4(
    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.EEGNetv4.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#L353

Parameters

(see source for parameter list)

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.