Advances on Real Time M/EEG Neural Feature Extraction
(2025) 38th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2025 In Proceedings - IEEE Symposium on Computer-Based Medical Systems p.337-338- 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.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/c84ffdea-274e-4c6a-af77-ccc47d185104
- author
- organization
- publishing date
- 2025
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- MlEEG, Neural feature, Neurofeedback
- host publication
- Proceedings - 2025 IEEE 38th International Symposium on Computer-Based Medical Systems, CBMS 2025
- series title
- Proceedings - IEEE Symposium on Computer-Based Medical Systems
- editor
- Rodriguez-Gonzalez, Alejandro ; Sicilia, Rosa ; Prieto-Santamaria, Lucia ; Papadopoulos, George A. ; Guarrasi, Valerio ; Cazzolato, Mirela Teixeira and Kane, Bridget
- pages
- 2 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 38th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2025
- conference location
- Madrid, Spain
- conference dates
- 2025-06-18 - 2025-06-20
- external identifiers
-
- scopus:105010604969
- ISSN
- 1063-7125
- ISBN
- 9798331526108
- DOI
- 10.1109/CBMS65348.2025.00074
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © 2025 IEEE.
- id
- c84ffdea-274e-4c6a-af77-ccc47d185104
- date added to LUP
- 2026-01-19 14:59:21
- date last changed
- 2026-01-19 14:59:21
@inproceedings{c84ffdea-274e-4c6a-af77-ccc47d185104,
abstract = {{<p>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.</p>}},
author = {{Shabestari, Payam S. and Ribes, Delphine and Defayes, Lara and Cai, Danpeng and Groves, Emily and Behjat, Harry H. and Van De Ville, Dimitri and Kleinjung, Tobias and Naas, Adrian and Henchoz, Nicolas and Sonderegger, Andreas and Neff, Patrick}},
booktitle = {{Proceedings - 2025 IEEE 38th International Symposium on Computer-Based Medical Systems, CBMS 2025}},
editor = {{Rodriguez-Gonzalez, Alejandro and Sicilia, Rosa and Prieto-Santamaria, Lucia and Papadopoulos, George A. and Guarrasi, Valerio and Cazzolato, Mirela Teixeira and Kane, Bridget}},
isbn = {{9798331526108}},
issn = {{1063-7125}},
keywords = {{MlEEG; Neural feature; Neurofeedback}},
language = {{eng}},
pages = {{337--338}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
series = {{Proceedings - IEEE Symposium on Computer-Based Medical Systems}},
title = {{Advances on Real Time M/EEG Neural Feature Extraction}},
url = {{http://dx.doi.org/10.1109/CBMS65348.2025.00074}},
doi = {{10.1109/CBMS65348.2025.00074}},
year = {{2025}},
}