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

TROCELLIER, David; N'KAOUA, Bernard; LOTTE, Fabien

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TROCELLIER, David
Popular interaction with 3d content [Potioc]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]

N'KAOUA, Bernard

Bordeaux population health [BPH]

LOTTE, Fabien

Popular interaction with 3d content [Potioc]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]

Langue

Communication dans un congrès

Ce document a été publié dans

Proceedings of the 9th Graz Brain-Computer Interface Conference 2024 Join Forces - Increase Performance, GBCIC 2024 - 9th Graz Brain-Computer Interface Conference 2024, 2024-09-09, Graz. 2024-09-12

Résumé en anglais

Brain-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.< Réduire

Mots clés en anglais

BCI

Motor Imagerie

Performance

Neurophysiological predictors

URI

https://oskar-bordeaux.fr/handle/20.500.12278/201615

Unités de recherche

Bordeaux Population Health Research Center (BPH) - UMR 1219

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