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

Abstract

<i>Objective.</i>Studying motor units is essential for understanding motor control, the detection of neuromuscular disorders and the control of human-machine interfaces. Individual motor unit firings are currently identified<i>in vivo</i>by decomposing electromyographic (EMG) signals. Due to our body's properties and anatomy, individual motor units can only be separated to a limited extent with surface EMG. Unlike electrical signals, magnetic fields do not interact with human tissues. This physical property and the emerging technology of quantum sensors make magnetomyography (MMG) a highly promising methodology. However, the full potential of MMG to study neuromuscular physiology has not yet been explored.<i>Approach.</i>In this work, we perform<i>in silico</i>trials that combine a biophysical model of EMG and MMG with state-of-the-art algorithms for the decomposition of motor units. This allows the prediction of an upper-bound for the motor unit decomposition accuracy.<i>Main results.</i>It is shown that non-invasive high-density MMG data is superior over comparable high-density surface EMG data for the robust identification of the discharge patterns of individual motor units. Decomposing MMG instead of EMG increased the number of identifiable motor units by 76%. Notably, MMG exhibits a less pronounced bias to detect superficial motor units.<i>Significance.</i>The presented simulations provide insights into methods to study the neuromuscular system non-invasively and<i>in vivo</i>that would not be easily feasible by other means. Hence, this study provides guidance for the development of novel biomedical technologies.