| Paper ID | BIO-1.3 |
| Paper Title |
RIEMANNIAN GEOMETRY ON CONNECTIVITY FOR CLINICAL BCI |
| Authors |
Marie-Constance Corsi, Aramis lab, Paris Brain Institute, France; Florian Yger, LAMSADE, Univ Paris-Dauphine, France; Sylvain Chevallier, LISV, Univ Paris-Saclay, France; Camille Noûs, Cogitamus, CNRS, France |
| Session | BIO-1: Brain-Computer Interfaces |
| Location | Gather.Town |
| Session Time: | Tuesday, 08 June, 13:00 - 13:45 |
| Presentation Time: | Tuesday, 08 June, 13:00 - 13:45 |
| Presentation |
Poster
|
| Topic |
Biomedical Imaging and Signal Processing: [BIO-BCI] Brain/human-computer interfaces |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| 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 1st on the task 1 of the competition. |
