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Extending Riemannian Brain-Computer Interface to Functional Connectivity Estimators

Chevallier, Sylvain; Corsi, Marie-Constance; Yger, Florian; Noûs, Camille (2020), Extending Riemannian Brain-Computer Interface to Functional Connectivity Estimators, IROS Workshop on Bringing geometric methods to robot learning, optimization and control, 2020-10, Las Vegas, NV / Virtual, United States

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Type
Communication / Conférence
Date
2020
Conference title
IROS Workshop on Bringing geometric methods to robot learning, optimization and control
Conference date
2020-10
Conference city
Las Vegas, NV / Virtual
Conference country
United States
Metadata
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Author(s)
Chevallier, Sylvain
Corsi, Marie-Constance
Yger, Florian
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Noûs, Camille
Abstract (EN)
This abstract describes a novel approach for handling brain-computer interfaces (BCI), that could be used for robotic applications. State-of-the-art approaches rely on the classification of covariance matrices in the manifold of symmetric positive-definite matrices. Functional connectivity estimators have demonstrated their reliability and are good candidates to improve the classification accuracy of covariance-based methods. This abstract explores possible application of functional connectivity in Riemannian BCI.
Subjects / Keywords
Brain-Computer