Functional Connectivity Analysis in Motor-Imagery Brain Computer Interfaces.

ABSTRACT: 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

SUBMITTER: Leeuwis N 

PROVIDER: S-EPMC8555469 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature