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Validation de marqueurs neurophysiologique des performances en BCI sur une large base de donnée open source

dc.rights.licenseopenen_US
hal.structure.identifierPopular interaction with 3d content [Potioc]
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
dc.contributor.authorTROCELLIER, David
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorN'KAOUA, Bernard
hal.structure.identifierPopular interaction with 3d content [Potioc]
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
dc.contributor.authorLOTTE, Fabien
IDREF: 139468617
dc.contributor.editorMULLER-PUTZ, Gernot R.
dc.date.accessioned2024-09-17T07:28:29Z
dc.date.available2024-09-17T07:28:29Z
dc.date.issued2024-09-12
dc.date.conference2024-09-09
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/201615
dc.description.abstractEnBrain-computer interfaces (BCI) are systems that process brain activity to decode specific commands from it such as motor imagery patterns generated when users imagine movements. Despite the growing interest in BCI, they present significant challenges, notably in decoding distinct neural patterns, due to considerable variability across and within users. The literature showed that various predictors were correlated with subject’s BCI performance. Among these indicators, neurophysiological predictors appeared to be the most effective, although studies generally involved small samples and results were not always replicated, thus questioning their reliability. In our study, we used a large dataset with 85 subjects to analyse the relationship between different predictors identified in the literature and BCI performance. Our findings reveal that only four of the six predictors tested could be replicated on this dataset. These results underscore the necessity of validating literature findings to ensure the reliability and applicability of such predictors.
dc.language.isoFRen_US
dc.rights.urihttp://hal.archives-ouvertes.fr/licences/publicDomain/
dc.subject.enBCI
dc.subject.enMotor Imagerie
dc.subject.enPerformance
dc.subject.enNeurophysiological predictors
dc.titleValidation de marqueurs neurophysiologique des performances en BCI sur une large base de donnée open source
dc.title.enValidating neurophysiological predictors of BCI performance on a large open source dataset
dc.typeCommunication dans un congrèsen_US
dc.subject.halInformatique [cs]/Interface homme-machine [cs.HC]en_US
dc.subject.halSciences du Vivant [q-bio]/Neurosciences [q-bio.NC]/Sciences cognitivesen_US
bordeaux.hal.laboratoriesBordeaux Population Health Research Center (BPH) - UMR 1219en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionINSERMen_US
bordeaux.conference.titleGBCIC 2024 - 9th Graz Brain-Computer Interface Conference 2024en_US
bordeaux.countryaten_US
bordeaux.title.proceedingProceedings of the 9th Graz Brain-Computer Interface Conference 2024 Join Forces - Increase Performanceen_US
bordeaux.conference.cityGrazen_US
bordeaux.import.sourcehal
hal.identifierhal-04696176
hal.version1
hal.invitednonen_US
hal.proceedingsouien_US
hal.conference.organizerBrain-Computer Interface (BCI) Societyen_US
hal.conference.end2024-09-12
hal.popularnonen_US
hal.audienceInternationaleen_US
hal.exportfalse
workflow.import.sourcehal
dc.rights.ccPas de Licence CCen_US
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.title=Validation%20de%20marqueurs%20neurophysiologique%20des%20performances%20en%20BCI%20sur%20une%20large%20base%20de%20donn%C3%A9e%20open%20source&rft.atitle=Validation%20de%20marqueurs%20neurophysiologique%20des%20performances%20en%20BCI%20sur%20une%20large%20base%20de%20donn%C3%A9e%20open%20source&rft.date=2024-09-12&rft.au=TROCELLIER,%20David&N'KAOUA,%20Bernard&LOTTE,%20Fabien&rft.genre=unknown


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