Speedup computation of HD-sEMG signals using a motor unit-specific electrical source model

Area of Science:

• Biomedical Engineering
• Computational Neuroscience
• Electrophysiology

Background:

• Muscle contraction modeling is crucial but computationally intensive.
• Increasing complexity of bio-reliable models leads to prohibitive computation times.
• Efficient generation of high-density surface electromyogram (HD-sEMG) signals is needed for research and clinical applications.

Purpose of the Study:

• To significantly reduce the computation time for HD-sEMG signal generation.
• To introduce a novel motor unit (MU)-specific electrical source model.
• To maintain bio-reliability while improving computational efficiency.

Main Methods:

• Developed a new MU-specific electrical source model based on constituent fibers.
• Simulated hundreds of MU action potentials (MUAPs) using both fiber and MU-specific models.
• Calculated normalized root mean square error (NRMSE) to compare signal fidelity.
• Assessed computation time reduction compared to traditional fiber electrical source models.

Main Results:

• HD-sEMG signal generation time was reduced by approximately 90%.
• The MU-specific model introduced less than 2% error compared to the fiber electrical source model.
• Physiological muscle HD-sEMG signals (hundreds of MUs) can be simulated in under an hour on a standard workstation.

Conclusions:

• The MU-specific electrical source model offers a computationally efficient alternative for HD-sEMG generation.
• This method significantly accelerates simulations without compromising bio-reliability.
• Enables advanced muscle contraction modeling and HD-sEMG analysis in practical timeframes.