Enhancement of EEG–EMG coupling detection using corticomuscular coherence with spatial–temporal optimization

Abstract Objective . Corticomuscular coherence (CMC) is widely used to detect and quantify the coupling between motor cortex and effector muscles. It is promisingly used in human–machine interaction (HMI) supported rehabilitation training to promote the closed-loop motor control for stroke patients. However, suffering from weak coherence features and low accuracy in contingent neurofeedback, its application to HMI rehabilitation robots is currently limited. In this paper, we propose the concept of spatial–temporal CMC (STCMC), which is the coherence by refining CMC with delay compensation and spatial optimization. Approach . The proposed STCMC method measures the coherence between electroencephalogram (EEG) and electromyogram (EMG) in the multivariate spaces. Specifically, we combined delay compensation and spatial optimization to maximize the absolute value of the coherence. Then, we tested the reliability and effectiveness of STCMC on neurophysiological data of force tracking tasks. Main results . Compared with CMC, STCMC not only enhanced the coherence significantly between brain and muscle signals, but also produced higher classification accuracy. Further analysis showed that temporal and spatial parameters estimated by the STCMC reflected more detailed brain topographical patterns, which emphasized the different roles between the contralateral and ipsilateral hemisphere. Significance . This study integrates delay compensation and spatial optimization to give a new perspective for corticomuscular coupling analysis. It is also feasible to design robotic neurorehabilitation paradigms by the proposed method.