README.md

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

# 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

```bash
pip install braindecode
```

```python
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

![EEGMiner architecture](https://content.cld.iop.org/journals/1741-2552/21/3/036010/revision2/jnead44d7f1_hr.jpg)


## 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:

```bibtex
@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.