Large scale investigation of the effect of gender on mu rhythm suppression in motor imagery brain-computer interfaces.

BCI inefficiency Brain-computer interface (BCI) gender motor imagery (MI) mu suppression

Journal

Brain computer interfaces (Abingdon, England)
ISSN: 2326-263X
Titre abrégé: Brain Comput Interfaces (Abingdon)
Pays: England
ID NLM: 101625088

Informations de publication

Date de publication:
2024
Historique:
medline: 2 10 2024
pubmed: 2 10 2024
entrez: 2 10 2024
Statut: epublish

Résumé

The utmost issue in Motor Imagery Brain-Computer Interfaces (MI-BCI) is the BCI poor performance known as 'BCI inefficiency'. Although past research has attempted to find a solution by investigating factors influencing users' MI-BCI performance, the issue persists. One of the factors that has been studied in relation to MI-BCI performance is gender. Research regarding the influence of gender on a user's ability to control MI-BCIs remains inconclusive, mainly due to the small sample size and unbalanced gender distribution in past studies. To address these issues and obtain reliable results, this study combined four MI-BCI datasets into one large dataset with 248 subjects and equal gender distribution. The datasets included EEG signals from healthy subjects from both gender groups who had executed a right- vs. left-hand motor imagery task following the Graz protocol. The analysis consisted of extracting the Mu Suppression Index from C3 and C4 electrodes and comparing the values between female and male participants. Unlike some of the previous findings which reported an advantage for female BCI users in modulating mu rhythm activity, our results did not show any significant difference between the Mu Suppression Index of both groups, indicating that gender may not be a predictive factor for BCI performance.

Identifiants

pubmed: 39355516
doi: 10.1080/2326263X.2024.2345449
pii: 2345449
pmc: PMC11441392
doi:

Types de publication

Journal Article

Langues

eng

Pagination

87-97

Informations de copyright

© 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Auteurs

Valentina Gamboa von Groll (VG)

Department of Cognitive Science and AI, Tilburg University, Tilburg, Netherlands.

Nikki Leeuwis (N)

Department of Cognitive Science and AI, Tilburg University, Tilburg, Netherlands.

Sébastien Rimbert (S)

Inria Center at the University of Bordeaux / LaBRI, Talence, France.

Aline Roc (A)

Inria Center at the University of Bordeaux / LaBRI, Talence, France.

Léa Pillette (L)

Department of Virtual Reality, Virtual Humans, Interactions and Robotics, University of Rennes, Inria, CNRS, France.

Fabien Lotte (F)

Inria Center at the University of Bordeaux / LaBRI, Talence, France.

Maryam Alimardani (M)

Department of Cognitive Science and AI, Tilburg University, Tilburg, Netherlands.

Classifications MeSH