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Extending Riemannian Brain-Computer Interface to Functional Connectivity Estimators

dc.contributor.authorChevallier, Sylvain
dc.contributor.authorCorsi, Marie-Constance
HAL ID: 21742
hal.structure.identifierLaboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
dc.contributor.authorYger, Florian
dc.contributor.authorNoûs, Camille
dc.date.accessioned2021-11-29T14:41:45Z
dc.date.available2021-11-29T14:41:45Z
dc.date.issued2020
dc.identifier.urihttps://basepub.dauphine.psl.eu/handle/123456789/22291
dc.language.isoenen
dc.subjectBrain-Computeren
dc.subject.ddc004en
dc.titleExtending Riemannian Brain-Computer Interface to Functional Connectivity Estimatorsen
dc.typeCommunication / Conférence
dc.description.abstractenThis 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.en
dc.subject.ddclabelInformatique généraleen
dc.relation.conftitleIROS Workshop on Bringing geometric methods to robot learning, optimization and controlen
dc.relation.confdate2020-10
dc.relation.confcityLas Vegas, NV / Virtualen
dc.relation.confcountryUnited Statesen
dc.relation.forthcomingnonen
dc.description.ssrncandidatenon
dc.description.halcandidatenonen
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.relation.Isversionofjnlpeerreviewednonen
dc.date.updated2021-11-29T14:36:53Z
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