Metadata-Version: 2.4
Name: gedai
Version: 0.1.0
Summary: GEDAI in python.
Author-email: Tomas Ros <tomas.ros@gmail.com>, Victor Férat <victor.ferat@live.fr>
Maintainer-email: Victor Férat <victor.ferat@live.fr>
Project-URL: documentation, https://github.com/neurotuning/gedai
Project-URL: homepage, https://github.com/neurotuning/gedai
Project-URL: source, https://github.com/neurotuning/gedai
Project-URL: tracker, https://github.com/neurotuning/gedai/issues
Keywords: python,gedai
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[![doc](https://github.com/neurotuning/gedai/actions/workflows/doc.yaml/badge.svg?branch=main)](https://github.com/neurotuning/gedai/actions/workflows/doc.yaml)
[![DOI:10.1101/2025.10.04.680449v1](http://img.shields.io/badge/DOI-10.1101/2025.10.04.680449-green.svg)](https://doi.org/10.1101/2025.10.04.680449)
[![DOI:10.1101/2025.10.04.680449v1](http://img.shields.io/badge/LIENSE-PolyFormNoncommercial_License_1.0.0-green.svg)](https://polyformproject.org/licenses/noncommercial/1.0.0)
[![tests](https://github.com/neurotuning/gedai/actions/workflows/pytest.yaml/badge.svg?branch=main)](https://github.com/neurotuning/gedai/actions/workflows/pytest.yaml)
[![codecov](https://codecov.io/gh/neurotuning/gedai/graph/badge.svg?token=6JE0JDBCYB)](https://codecov.io/gh/neurotuning/gedai)

# GEDAI denoising in python
![GEDAI_logo](https://github.com/user-attachments/assets/5e06d5d4-1e68-4a74-b47c-cf59e850f379)


For more details about the GEDAI algorithm, please refer to the [MATLAB implementation](https://github.com/neurotuning/GEDAI-master).

## Documentation
[![doc](https://github.com/neurotuning/GEDAI/actions/workflows/doc.yaml/badge.svg?branch=main)](https://github.com/neurotuning/GEDAI/actions/workflows/doc.yaml)

Detailed documentation can be found on [GEDAI website](https://neurotuning.github.io/gedai).

## 📜 Citation
[![DOI:10.1101/2025.10.04.680449v1](http://img.shields.io/badge/DOI-10.1101/2025.10.04.680449-green.svg)](https://doi.org/10.1101/2025.10.04.680449)


If you use GEDAI in your research, please cite the original publication:

> 
>*Return of the GEDAI: Unsupervised EEG Denoising based on Leadfield Filtering* (2025)  [bioRxiv]. [[DOI/Link to paper](https://www.biorxiv.org/content/10.1101/2025.10.04.680449v1)]  
>Ros, T, Férat, V., Huang, Y., Colangelo, C., Kia S.M., Wolfers T., Vulliemoz, S., & Michela, A. 
>

## License
[![DOI:10.1101/2025.10.04.680449v1](http://img.shields.io/badge/LIENSE-PolyFormNoncommercial_License_1.0.0-green.svg)](https://polyformproject.org/licenses/noncommercial/1.0.0)


You may use this software under the terms of the PolyForm Noncommercial License 1.0.0 [LICENSE](LICENSE). This is suitable for personal use, research, or evaluation.

**Commercial License**  
If you wish to use this software in a commercial or proprietary application without being bound by terms of the PolyForm Noncommercial License 1.0.0, you must purchase a commercial license. The core algorithms in this repository are the subject of a pending patent application, and a commercial license includes a grant for patent rights.  

## 📧 Contact

For any questions or enquiries, please contact:
Tomas Ros - tomas.ros@unige.ch
Victor Férat: victor.ferat@fcbg.ch

## Acknowledgements
We are gratefully supported by the Center for Biomedical Imaging (CIBM), the Swiss National Science Foundation (SNSF), Unitec and the M/EEG & NMOD Platform, Fondation Campus Biotech Geneva, Geneva,.

[![cibm](https://github.com/user-attachments/assets/0e67c6b9-0dae-415f-8321-6b8148862e85)](https://cibm.ch/)
[![snf](https://github.com/user-attachments/assets/9db9e0b5-05dd-488e-9730-9abea2f7e8af)](https://www.snf.ch/en)
[![unitec](https://github.com/user-attachments/assets/5417b534-6d3c-495a-8c24-70f65442221b)](https://www.unige.ch/unitec/)
[<img src="https://mbskblppw.preview.infomaniak.website/wp-content/uploads/2024/08/icon_website-1.png" width="140"/> ](https://meeg-nmod.fcbg.ch/)

