Predicting electromyographic signals under realistic conditions using a multiscale chemo-electro-mechanical finite...

Area of Science:

• Biomechanics
• Computational Biology
• Electrophysiology

Background:

• Electromyographic (EMG) signals are crucial for understanding muscle function.
• Existing models often lack detailed biophysical underpinnings for accurate physiological representation.

Purpose of the Study:

• To develop a novel multiscale finite element framework for modeling EMG signals.
• To incorporate biophysical details of excitation-contraction coupling and action potential propagation.
• To simulate muscle deformation and its impact on EMG.

Main Methods:

• A multiscale finite element approach combining half-sarcomere biophysics, action potential propagation, and continuum mechanics.
• Modeling of force generation, muscle deformation, and EMG prediction.
• Simulation of idealized and realistic muscle geometries (e.g., human tibialis anterior).

Main Results:

• The model accurately predicts EMG signal changes due to physiological factors like membrane fatigue (40% amplitude decrease, 15% velocity decrease).
• Demonstrated capability to simulate contraction-induced deformations in muscle tissue.
• Successfully modeled complex geometries, fiber architectures, and heterogeneities in the tibialis anterior muscle.

Conclusions:

• The proposed framework provides a robust tool for biophysically accurate EMG signal modeling.
• It can account for complex physiological phenomena including fatigue and deformation.
• The model's ability to handle realistic muscle geometries and architectures enhances its applicability in neuromuscular research.