Effective Correlates of Motor Imagery Performance based on Default Mode Network in Resting-State

People with low performance in motor imagery brain-computer interfaces (MI-BCIs) show a 23% performance gap compared to high performers, and a specific brain connectivity pattern during resting-state EEG—from the right lateral parietal to the left lateral parietal region—correlates negatively with MI performance (r = -0.37). These results suggest that resting-state effective connectivity may help explain why some individuals cannot effectively use MI-BCIs, a phenomenon known as BCI-illiteracy, and could guide alternative approaches tailored to the user.

Study at a glance

Abstract

Motor imagery based brain-computer interfaces (MI-BCIs) allow the control of devices and communication by imagining different muscle movements. However, most studies have reported a problem of "BCI-illiteracy" that does not have enough performance to use MI-BCI. Therefore, understanding subjects with poor performance and finding the cause of performance variation is still an important challenge. In this study, we proposed predictors of MI performance using effective connectivity in resting-state EEG. As a result, the high and low MI performance groups had a significant difference as 23% MI performance difference. We also found that connection from right lateral parietal to left lateral parietal in resting-state EEG was correlated significantly with MI performance (r = -0.37). These findings could help to understand BCI-illiteracy and to consider alternatives that are appropriate for the subject.