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Communication Dans Un Congrès Année : 2020

Extending Riemannian Brain-Computer Interface to Functional Connectivity Estimators

Résumé

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.
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Dates et versions

hal-03015390 , version 1 (19-11-2020)

Identifiants

  • HAL Id : hal-03015390 , version 1

Citer

Sylvain Chevallier, Marie-Constance Corsi, Florian Yger, Camille Noûs. Extending Riemannian Brain-Computer Interface to Functional Connectivity Estimators. IROS Workshop on Bringing geometric methods to robot learning, optimization and control, Oct 2020, Las Vegas, NV / Virtual, United States. ⟨hal-03015390⟩
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