A simulation study on the relation between the motor unit depth and action potential from multi-channel surface electromyography recordings

To investigate the spatial information of individual motor unit (MUs) using multi-channel surface electromyography (EMG) decomposition. The K-means clustering convolution kernel compensation (KmCKC) approach was employed to detect the innervation pulse trains (IPTs) from the simulated surface EMG signals, and the motor unit action potentials (MUAPs) were evaluated using the spike-triggered average (STA) technique. The relationships between the features of MUAP and MU depth were determinated with a least square fitting method. The errors of peak-to-peak (PTP) amplitude of reconstructed MUAPs were less than 5.73%, even with 0 dB signal-to-noise (SNR). The fitting errors with nonlinear model were less than 5.55% for SNRs higher than 20 dB. The results show that it is possible to provide a useful method for estimating MU depth from surface EMG recordings. It is expected to extend the applicability of surface EMG technique to more challenging clinical applications.