Enhancing Brain-Computer Interface Performance by Incorporating Brain-to-Brain Coupling.


Journal

Cyborg and bionic systems (Washington, D.C.)
ISSN: 2692-7632
Titre abrégé: Cyborg Bionic Syst
Pays: United States
ID NLM: 9918400086506676

Informations de publication

Date de publication:
2024
Historique:
received: 15 11 2023
accepted: 24 03 2024
medline: 29 4 2024
pubmed: 29 4 2024
entrez: 29 4 2024
Statut: epublish

Résumé

Human cooperation relies on key features of social interaction in order to reach desirable outcomes. Similarly, human-robot interaction may benefit from integration with human-human interaction factors. In this paper, we aim to investigate brain-to-brain coupling during motor imagery (MI)-based brain-computer interface (BCI) training using eye-contact and hand-touch interaction. Twelve pairs of friends (experimental group) and 10 pairs of strangers (control group) were recruited for MI-based BCI tests concurrent with electroencephalography (EEG) hyperscanning. Event-related desynchronization (ERD) was estimated to measure cortical activation, and interbrain functional connectivity was assessed using multilevel statistical analysis. Furthermore, we compared BCI classification performance under different social interaction conditions. In the experimental group, greater ERD was found around the contralateral sensorimotor cortex under social interaction conditions compared with MI without any social interaction. Notably, EEG channels with decreased power were mainly distributed around the frontal, central, and occipital regions. A significant increase in interbrain coupling was also found under social interaction conditions. BCI decoding accuracies were significantly improved in the eye contact condition and eye and hand contact condition compared with the no-interaction condition. However, for the strangers' group, no positive effects were observed in comparisons of cortical activations between interaction and no-interaction conditions. These findings indicate that social interaction can improve the neural synchronization between familiar partners with enhanced brain activations and brain-to-brain coupling. This study may provide a novel method for enhancing MI-based BCI performance in conjunction with neural synchronization between users.

Identifiants

pubmed: 38680535
doi: 10.34133/cbsystems.0116
pii: 0116
pmc: PMC11052607
doi:

Types de publication

Journal Article

Langues

eng

Pagination

0116

Informations de copyright

Copyright © 2024 Tianyu Jia et al.

Déclaration de conflit d'intérêts

Competing interests: The authors declare that they have no competing interests.

Auteurs

Tianyu Jia (T)

Lab of Intelligent and Biomimetic Machinery, Department of Mechanical Engineering, Tsinghua University, Beijing, China.
Department of Bioengineering, Imperial College London, London, UK.

Jingyao Sun (J)

Lab of Intelligent and Biomimetic Machinery, Department of Mechanical Engineering, Tsinghua University, Beijing, China.

Ciarán McGeady (C)

Department of Bioengineering, Imperial College London, London, UK.

Linhong Ji (L)

Lab of Intelligent and Biomimetic Machinery, Department of Mechanical Engineering, Tsinghua University, Beijing, China.

Chong Li (C)

School of Clinical Medicine, Tsinghua University, Beijing, China.
Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing, China.
Lab of Intelligent and Biomimetic Machinery, Department of Mechanical Engineering, Tsinghua University, Beijing, China.

Classifications MeSH