Synchronous analyses between electroencephalogram and surface electromyogram based on motor imagery and motor...

Area of Science:

• Neuroscience
• Biomedical Engineering
• Rehabilitation Science

Background:

• The brain's control over muscle activity results in coherent electroencephalogram (EEG) and surface electromyogram (sEMG) signals.
• Understanding this coupling is crucial for assessing neural function and guiding rehabilitation.

Purpose of the Study:

• To elucidate the coupling relationship between EEG and sEMG signals using diverse analytical techniques.
• To propose an enhanced EEG modification algorithm for improving EEG-sEMG coherence analysis.
• To identify the most effective coherence analysis methods for studying neural-muscle interactions.

Main Methods:

• Collected EEG and sEMG data during motor imagery and execution in healthy subjects and stroke patients.
• Developed an EEG modification algorithm based on sEMG peak positions to enhance coherence.
• Employed signal synchronization analysis and wavelet coherence analysis with time spectrum estimation.
• Investigated linear correlations in frequency domain components of EEG and sEMG signals.

Main Results:

• Wavelet coherence analysis effectively captures the temporal dynamics of EEG-sEMG coherence.
• Significant EEG-sEMG coherence during motor imagery was observed in alpha and beta bands.
• During motor execution, significant coherence occurred pre- and intra-task, primarily in alpha, beta, and gamma bands.

Conclusions:

• The findings provide a theoretical framework for optimizing neurorehabilitation training.
• This study introduces a novel method for evaluating functional states in neural movement rehabilitation.