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Architecture-only repository. Documents thebraindecode.models.EEGMinerclass. No pretrained weights are distributed here. Instantiate the model and train it on your own data.
pip install braindecode1from braindecode.models import EEGMiner
2
3model = EEGMiner(
4 n_chans=22,
5 sfreq=250,
6 input_window_seconds=4.0,
7 n_outputs=4,
8)
| 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. |
1@article{aristimunha2025braindecode,
2 title = {Braindecode: a deep learning library for raw electrophysiological data},
3 author = {Aristimunha, Bruno and others},
4 journal = {Zenodo},
5 year = {2025},
6 doi = {10.5281/zenodo.17699192},
7}