Riemannian Geometry on Connectivity for Clinical BCI

Marie-Constance Corsi; Florian Yger; Sylvain Chevallier; Camille Noûs

ICASSP 2021accepted0 citations

Riemannian Geometry on Connectivity for Clinical BCI

Marie-Constance Corsi, Florian Yger, Sylvain Chevallier, Camille Noûs

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 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> on the task 1 of the competition.

BibTeX

@inproceedings{icassp2021_riemanniangeomet,
  title = {Riemannian Geometry on Connectivity for Clinical BCI},
  author = {Marie-Constance Corsi and Florian Yger and Sylvain Chevallier and Camille Noûs},
  booktitle = {ICASSP 2021},
  year = {2021}
}