Advances on Real Time M/EEG Neural Feature Extraction
This paper introduces MNE-RT, a Python package designed for real-time neural feature extraction from magne-toencephalography (MEG) and electroencephalography (EEG) signals in Brain-Computer Interface (BCI) systems. The package incorporates efficient algorithms spanning traditional univariate metrics, such as frequency band power and entropy, to advanced bivariate connectivity measures. It is compatible with various recording systems, enabling the extraction of neural targets from brain signals in real time, with potential applications in enhancing neurofeedback efficacy.
Authors
- Hamid Behjat (ORCID: https://orcid.org/0000-0001-6729-6801)
- Dimitri Van De Ville (ORCID: https://orcid.org/0000-0002-2879-3861)
- Payam S. Shabestari (ORCID: https://orcid.org/0000-0002-2647-4891)
- Emily Groves (ORCID: https://orcid.org/0000-0002-4460-2455)
- Tobias Kleinjung (ORCID: https://orcid.org/0000-0003-3867-234X)
- Dong-Zhen Cai (ORCID: https://orcid.org/0000-0002-8992-3092)
- Delphine Ribes (ORCID: https://orcid.org/0000-0001-5527-1076)
- Lara Défayes (ORCID: https://orcid.org/0000-0002-5867-6199)
- Adrian Naas (ORCID: https://orcid.org/0000-0002-8662-5794)
- Patrick Neff (ORCID: https://orcid.org/0000-0003-3174-4910)
- Andreas Sonderegger (ORCID: https://orcid.org/0000-0003-0054-0544)
- Nicolas Henchoz (ORCID: https://orcid.org/0000-0002-4284-1321)
Institutions
- University of Fribourg (CH)
- Lund University (SE)
- University Hospital of Zurich (CH)
- École Polytechnique Fédérale de Lausanne (CH)
Publication Details
- Published
- 2025-06-18
- DOI
- https://doi.org/10.1109/cbms65348.2025.00074
- Primary Topic
- Neural Networks and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00