High-density magnetomyography is superior to high-density surface electromyography for motor unit decomposition: a...

Area of Science:

• Biomedical Engineering
• Neuroscience
• Signal Processing

Background:

• Motor unit analysis is crucial for understanding motor control, neuromuscular disorders, and human-machine interfaces.
• Surface electromyography (EMG) has limitations in resolving individual motor units due to anatomical and physiological constraints.
• Magnetomyography (MMG), leveraging magnetic field properties and quantum sensors, presents a promising non-invasive alternative for neuromuscular studies.

Purpose of the Study:

• To evaluate the potential of magnetomyography (MMG) for enhanced motor unit decomposition compared to surface electromyography (EMG).
• To predict the upper-bound accuracy for motor unit decomposition using both EMG and MMG signals through in silico trials.

Main Methods:

• Combined biophysical modeling of EMG and MMG signals with advanced motor unit decomposition algorithms.
• Performed in silico trials to simulate and analyze the performance of MMG and EMG in identifying individual motor unit discharge patterns.
• Utilized high-density MMG and EMG data for comparative analysis.

Main Results:

• High-density MMG data demonstrated superior performance over comparable EMG data for robust identification of individual motor unit discharge patterns.
• Decomposition of MMG signals resulted in a 76% increase in the number of identifiable motor units compared to EMG.
• MMG showed a reduced bias towards detecting superficial motor units, offering a more comprehensive analysis.

Conclusions:

• Non-invasive high-density MMG is a more effective method than EMG for identifying individual motor unit activity in silico.
• These findings provide critical insights for developing novel biomedical technologies for non-invasive in vivo neuromuscular system studies.
• MMG holds significant potential for advancing the study of neuromuscular physiology and clinical diagnostics.