The Spectral Footprint of Neural Activity: How MUAP Properties and Spike Train Variability Shape sEMG.

Area of Science:

• Neuroscience
• Biomedical Engineering
• Physiology

Background:

• Surface electromyographic (sEMG) signals arise from motor unit action potentials (MUAPs) and neural spike trains.
• The precise influence of neural spike timing on the sEMG spectrum remains incompletely understood.

Purpose of the Study:

• To develop a framework clarifying how neural timing variability and muscle properties impact the sEMG spectrum.
• To quantify the impact of firing rate, temporal jitter, and motor unit synchronization on spectral characteristics.

Main Methods:

• Simulated sEMG signals using a convolutional model with synthetic spike trains and MUAP templates.
• Varied parameters including firing rate, temporal jitter, and motor unit synchronization.
• Introduced extractability indices to assess neural activity visibility in the sEMG spectrum.

Main Results:

• MUAPs function as spectral filters, attenuating frequencies outside their bandwidth and limiting high firing rate detection.
• Temporal jitter disperses spectral energy and diminishes frequency peaks.
• Moderate motor unit synchronization enhances spectral visibility, counteracting some jitter effects.

Conclusions:

• Neural timing and muscle properties significantly shape sEMG spectral content.
• Findings provide a reference for interpreting sEMG spectral analysis in neuromuscular studies.
• Supports more informed application of spectral analysis in experimental and applied settings.