Exploring Corticomuscular Coupling During Active and Passive Movements using Electroencephalography and Surface Electromyography

Abstract Purpose The rehabilitation training of stroke patients based on their movement intentions can improve their chances of recovery because movement intention affords greater integration of the brain and muscle functions during training. Against this backdrop, in this study, we acquired the electroencephalography (EEG) and surface electromyography (sEMG) data related to the grasping movements of 14 healthy subjects in the active- and passive-movement modes to explore corticomuscular coupling in these modes. Methods Transfer entropy (TE) was calculated between EEG and EMG signals in different frequency bands. We also calculated the power spectral density (PSD) of EEG signals. In order to study the transmission of information between different brain regions, we also constructed a corticomuscular network (CMN) , and calculated various network metrics using graph theory.Results The power spectral density of active movement in the beta band was significantly greater than that of passive movement, and the spectral power ratio of the beta and alpha bands also increases. TE value of active movement was significantly higher than that of passive movement. The corticomuscular network metrics between these two movement modes were different.Conclusion Movement intention has a great influence on human movement. Active movement enhances the information interaction between brain regions and muscles, and higher information-transmission efficiency.