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

Area of Science:

• Neuroscience
• Biomedical Engineering
• Robotics

Background:

• Corticomuscular coherence (CMC) quantifies brain-muscle coupling, vital for human-machine interaction (HMI) in stroke rehabilitation.
• Current CMC methods face limitations in accuracy and feature detection for HMI rehabilitation robots.

Purpose of the Study:

• To propose and validate a novel spatial-temporal corticomuscular coherence (STCMC) method.
• To enhance the accuracy and reliability of brain-muscle signal coupling analysis for HMI applications.

Main Methods:

• Developed STCMC by integrating delay compensation and spatial optimization into CMC.
• Measured coherence between electroencephalogram (EEG) and electromyogram (EMG) in multivariate spaces.
• Validated STCMC using neurophysiological data from force tracking tasks.

Main Results:

• STCMC significantly enhanced coherence between brain and muscle signals compared to traditional CMC.
• STCMC achieved higher classification accuracy in neurofeedback tasks.
• Estimated STCMC parameters revealed detailed contralateral and ipsilateral hemispheric brain topographical patterns.

Conclusions:

• STCMC offers a novel perspective for corticomuscular coupling analysis by integrating temporal and spatial optimization.
• The proposed STCMC method is feasible for designing advanced robotic neurorehabilitation paradigms.