EEGMiner

EEGMiner from Ludwig et al (2024) [eegminer].

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

model = EEGMiner(
    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

Architecture

EEGMiner architecture

Parameters

Parameter Type Description
method str, default="plv" The method used for feature extraction. Options are: - "mag": Electrode-Wise band power of the filtered signals. - "corr": Correlation between filtered channels. - "plv": Phase Locking Value connectivity metric.
filter_f_mean list of float, default=[23.0, 23.0] Mean frequencies for the generalized Gaussian filters.
filter_bandwidth list of float, default=[44.0, 44.0] Bandwidths for the generalized Gaussian filters.
filter_shape list of float, default=[2.0, 2.0] Shape parameters for the generalized Gaussian filters.
group_delay tuple of float, default=(20.0, 20.0) Group delay values for the filters in milliseconds.
clamp_f_mean tuple of float, default=(1.0, 45.0) Clamping range for the mean frequency parameters.

References

  1. Ludwig, S., Bakas, S., Adamos, D. A., Laskaris, N., Panagakis, Y., & Zafeiriou, S. (2024). EEGMiner: discovering interpretable features of brain activity with learnable filters. Journal of Neural Engineering, 21(3), 036010.
  2. Ludwig, S., Bakas, S., Adamos, D. A., Laskaris, N., Panagakis, Y., & Zafeiriou, S. (2024). EEGMiner: discovering interpretable features of brain activity with learnable filters. https://github.com/SMLudwig/EEGminer/. Cogitat, Ltd. "Learnable filters for EEG classification." Patent GB2609265. https://www.ipo.gov.uk/p-ipsum/Case/ApplicationNumber/GB2113420.0

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.

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