Riemannian Geometry on Connectivity for Clinical BCI - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2021

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.
Fichier principal
Vignette du fichier
Poster_ICASSP_HAL.pdf (2.94 Mo) Télécharger le fichier
corsi2021.pdf (265.71 Ko) Télécharger le fichier
Origin Files produced by the author(s)
Origin Files produced by the author(s)

Dates and versions

hal-03202349 , version 1 (27-04-2021)

Identifiers

Cite

Camille Noûs, Marie-Constance Corsi, Sylvain Chevallier, Florian Yger. Riemannian Geometry on Connectivity for Clinical BCI. ICASSP 2021, Jun 2021, Toronto / Virtual, Canada. ⟨10.1109/ICASSP39728.2021.9414790⟩. ⟨hal-03202349⟩
266 View
357 Download

Altmetric

Share

Gmail Mastodon Facebook X LinkedIn More