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Riemannian Geometry on Connectivity for Clinical BCI

Abstract : 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.
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Contributor : Sylvain Chevallier <>
Submitted on : Tuesday, April 27, 2021 - 9:14:26 AM
Last modification on : Saturday, May 1, 2021 - 3:46:29 AM


  • HAL Id : hal-03202349, version 1


Camille Noûs, Marie-Constance Corsi, Sylvain Chevallier, Florian Yger. Riemannian Geometry on Connectivity for Clinical BCI. ICASSP 2021 - IEEE International Conference on Acoustics, Speech and Signal Processing, Jun 2021, Toronto / Virtual, Canada. ⟨hal-03202349⟩



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