Spatiotemporal Dynamics of Functional Connectivity Networks in EEG-based Motor Imagery

Understanding how motor imagery (MI) is represented in large-scale neural networks of the brain is important for improving electroencephalography (EEG)-based brain-computer interfaces (BCIs) and MI-based neurorehabilitation. This study examined frequency-specific functional connectivity during MI using EEG. MI demonstrated distinct network patterns: beta-band connectivity was higher during nondominant hand imagery, whereas alpha-band connectivity increased during dominant hand imagery. Subjective imagery ratings showed a statistically significant but weak correlation with global beta-band connectivity (r=0.07). These results imply that MI recruits distributed alpha- and beta-band networks and suggest that subjective experience reflects solely a limited portion of the underlying neural dynamics.