  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.
