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 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.
Highlights
Brain-Computer Interface (BCI) is a system that outputs an action based on the classification of the user’s brain waves
For the right hemisphere local connectivity, the test showed significantly different phase-locking value (PLV) between the BCI performance groups during the motor imagery phase in the alpha band (F(1,1,1,320) = 8.36, p = 0.005) there was no main effect for the MI hand or an interaction effect between the two factors
We examined the difference between high and low aptitude motor imagery BCI users in their EEG functional connectivity in three network scales (Global, Large, and Local scale) during the resting state, motor imagery task, and the transition between the two phases in each trial
Summary
Brain-Computer Interface (BCI) is a system that outputs an action based on the classification of the user’s brain waves. The technique enables humans to interact with the physical environment and external devices without having to move muscles (Wolpaw et al, 2002). Motor imagery BCI (MI-BCI) systems rely on the mental execution of a movement, which changes brain activity in the motor cortex (Pfurtscheller and Neuper, 2001). The system classifies these changes and thereby sends a command to the external device (Wolpaw et al, 2002). MI-BCIs have the advantage of not requiring external stimuli (as opposed to reactive BCIs) they require extensive training until the user is capable of producing ideal brain activity patterns for the system to classify (Wolpaw et al, 2002)
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