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Riemannian Geometry on Connectivity for Clinical BCI

Corsi, Marie-Constance; Yger, Florian; Chevallier, Sylvain; Noûs, Camille (2021), Riemannian Geometry on Connectivity for Clinical BCI, ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021-06, Toronto, Canada

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Type
Communication / Conférence
External document link
https://hal.archives-ouvertes.fr/hal-03202349
Date
2021
Conference title
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Conference date
2021-06
Conference city
Toronto
Conference country
Canada
Publisher
IEEE - Institute of Electrical and Electronics Engineers
Published in
Piscataway, NJ
ISBN
978-1-7281-7605-5
Publication identifier
Metadata
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Author(s)
Corsi, Marie-Constance
Yger, Florian
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Chevallier, Sylvain
Noûs, Camille
Abstract (EN)
Riemannian BCI based on EEG covariance have won many data competitions and achieved very high classification results on BCI datasets. To increase the accuracy of BCI systems, we propose an approach grounded on Riemannian geometry that extends this framework to functional connectivity measures. This paper describes the approach submitted to the Clinical BCI Challenge-WCCI2020 and that ranked 1 st on the task 1 of the competition.
Subjects / Keywords
Riemannian geometry; functional connectivity; ensemble learning; BCI