The Effects of Subthreshold Vibratory Noise on Cortical Activity During Motor Imagery.

brain–computer interface electroencephalography event-related desynchronization kinesthetic motor imagery machine learning vibratory stimulation virtual reality

Journal

Motor control
ISSN: 1087-1640
Titre abrégé: Motor Control
Pays: United States
ID NLM: 9706297

Informations de publication

Date de publication:
01 Jul 2023
Historique:
received: 18 05 2022
revised: 04 12 2022
accepted: 08 01 2023
medline: 28 6 2023
pubmed: 22 2 2023
entrez: 21 2 2023
Statut: epublish

Résumé

Previous studies have demonstrated that both visual and proprioceptive feedback play vital roles in mental practice of movements. Tactile sensation has been shown to improve with peripheral sensory stimulation via imperceptible vibratory noise by stimulating the sensorimotor cortex. With both proprioception and tactile sensation sharing the same population of posterior parietal neurons encoding within high-level spatial representations, the effect of imperceptible vibratory noise on motor imagery-based brain-computer interface is unknown. The objective of this study was to investigate the effects of this sensory stimulation via imperceptible vibratory noise applied to the index fingertip in improving motor imagery-based brain-computer interface performance. Fifteen healthy adults (nine males and six females) were studied. Each subject performed three motor imagery tasks, namely drinking, grabbing, and flexion-extension of the wrist, with and without sensory stimulation while being presented a rich immersive visual scenario through a virtual reality headset. Results showed that vibratory noise increased event-related desynchronization during motor imagery compared with no vibration. Furthermore, the task classification percentage was higher with vibration when the tasks were discriminated using a machine learning algorithm. In conclusion, subthreshold random frequency vibration affected motor imagery-related event-related desynchronization and improved task classification performance.

Identifiants

pubmed: 36801814
doi: 10.1123/mc.2022-0061
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

559-572

Auteurs

Kishor Lakshminarayanan (K)

Department of Sensors and Biomedical Engineering, School of Electronics Engineering, Vellore Institute of Technology, Vellore,India.

Rakshit Shah (R)

Cleveland State University, Cleveland, OH,USA.

Yifei Yao (Y)

Shanghai Jiao Tong University, Shanghai, SHG,China.

Deepa Madathil (D)

Jindal Institute of Behavioural Sciences, O. P. Jindal Global University, Haryana,India.

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Classifications MeSH