Effective Correlates of Motor Imagery Performance based on Default Mode Network in Resting-State
This addresses the challenge of poor performance in MI-BCIs for users, offering insights to tailor alternatives, though it is incremental as it builds on existing connectivity methods.
The study tackled the problem of 'BCI-illiteracy' in motor imagery brain-computer interfaces by proposing predictors based on resting-state EEG effective connectivity, finding a 23% performance difference between high and low groups and a significant correlation (r = -0.37) with connectivity from right to left lateral parietal regions.
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.