EEGMiner
EEGMiner from Ludwig et al (2024) [eegminer].
Architecture-only repository. Documents the
braindecode.models.EEGMinerclass. 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
- Full API reference: https://braindecode.org/stable/generated/braindecode.models.EEGMiner.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/eegminer.py#L21
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
- 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.
- 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.
