Riemannian Geometry On Connectivity For Clinical Bci
Marie-Constance Corsi, Florian Yger, Sylvain Chevallier, Camille Noûs
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08 Jun 2021
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 1st on the task 1 of the competition.
Chairs:
Erchin Serpedin
