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Advances on Real Time M/EEG Neural Feature Extraction

Year: 2025

Type: conference-paper

Abstract: 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.

Source: IEEE International Symposium on Computer-Based Medical Systems (CBMS)

DOI: 10.1109/cbms65348.2025.00074

Authors: Payam S. Shabestari, Delphine Ribes, Lara Défayes, Danpeng Cai, Emily Groves, Harry H. Behjat, Dimitri Van de Ville, Tobias Kleinjung, Adrian Naas, Nicolas Henchoz, Andreas Sonderegger, Patrick Neff

Institutions: University Hospital Zurich, École Polytechnique Fédérale de Lausanne, Lund University, University of Fribourg

Language: English

Topic: EEG and Brain-Computer Interfaces

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

Open Access status: green

OpenAlex ID: W4412030851

FWCI:

Cites: 4

Cited by:

Related to: 10