Cross-frequency Mutual Information for Cortico-muscular Coupling Detection.

Area of Science:

• Neuroscience
• Biomedical Engineering
• Signal Processing

Background:

• Corticomuscular coherence (CMC) traditionally assesses brain-muscle interactions but is limited to linear associations.
• Existing CMC methods often lack sensitivity, failing to detect significant coherence in individuals with good motor skills.

Purpose of the Study:

• To introduce a novel method, Mutual Information in Modes (MIM), for detecting both linear and nonlinear corticomuscular communications.
• To improve the sensitivity and scope of brain-muscle interaction analysis.

Main Methods:

• The MIM method integrates Variational Mode Extraction (VME) to isolate neural oscillation modes from electroencephalography (EEG) and electromyography (EMG) signals.
• Mutual Information (MI) is applied to quantify dependencies within and across these extracted modes.

Main Results:

• Simulated data confirmed the feasibility of the MIM method.
• Analysis of neurophysiological data showed MIM effectively captures frequency-specific EEG-EMG interactions.
• MIM demonstrated proficiency in identifying nonlinear cross-frequency couplings across different neural oscillation bands.

Conclusions:

• The MIM method offers a more sensitive approach to analyzing corticomuscular communication by incorporating nonlinear dynamics.
• This novel methodology provides a valuable tool for advancing the understanding of motor control and broader neurophysiological processes.