Functional Connectivity Analysis in Motor-Imagery Brain Computer Interfaces

Motor Imagery BCI systems have a high rate of users that are not capable of modulating their brain activity accurately enough to communicate with the system. Several studies have identified psychological, cognitive, and neurophysiological measures that might explain this MI-BCI inefficiency. Traditi...

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Bibliographic Details
Published in:Frontiers in human neuroscience 2021-10, Vol.15, p.732946-732946
Main Authors: Leeuwis, Nikki, Yoon, Sue, Alimardani, Maryam
Format: Article
Language:English
Subjects:
Algorithms
BCI inefficiency
brain computer interface (BCI)
Brain research
Classification
Cognitive ability
Electroencephalography
electroencephalography (EEG)
functional connectivity (FC)
Hemispheric laterality
Human Neuroscience
Interfaces
Mental task performance
motor imagery (MI)
Neural networks
phase synchronization
Sensorimotor system
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Summary:Motor Imagery BCI systems have a high rate of users that are not capable of modulating their brain activity accurately enough to communicate with the system. Several studies have identified psychological, cognitive, and neurophysiological measures that might explain this MI-BCI inefficiency. Traditional research had focused on mu suppression in the sensorimotor area in order to classify imagery, but this does not reflect the true dynamics that underlie motor imagery. Functional connectivity reflects the interaction between brain regions during the MI task and resting-state network and is a promising tool in improving MI-BCI classification. In this study, 54 novice MI-BCI users were split into two groups based on their accuracy and their functional connectivity was compared in three network scales (Global, Large and Local scale) during the resting-state, left vs. right-hand motor imagery task, and the transition between the two phases. Our comparison of High and Low BCI performers showed that in the alpha band, functional connectivity in the right hemisphere was increased in High compared to Low aptitude MI-BCI users during motor imagery. These findings contribute to the existing literature that indeed connectivity might be a valuable feature in MI-BCI classification and in solving the MI-BCI inefficiency problem.
ISSN:1662-5161
1662-5161
DOI:10.3389/fnhum.2021.732946