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Objective: Studying motor units (MUs) is essential for understanding motorcontrol, the detection of neuromuscular disorders and the control ofhuman-machine interfaces. Individual motor unit firings are currentlyidentified in vivo by decomposing electromyographic (EMG) signals. Due to ourbody's properties and anatomy, individual motor units can only be separated toa limited extent with surface EMG. Unlike electrical signals, magnetic fieldsdo not interact with human tissues. This physical property and the emergingtechnology of quantum sensors make magnetomyography (MMG) a highly promisingmethodology. However, the full potential of MMG to study neuromuscularphysiology has not yet been explored. Approach: In this work, we perform insilico trials that combine a biophysical model of EMG and MMG withstate-of-the-art algorithms for the decomposition of motor units. This allowsthe prediction of an upper-bound for the motor unit decomposition accuracy.Main results: It is shown that non-invasive high-density MMG data is superiorover comparable high-density surface EMG data for the robust identification ofthe discharge patterns of individual motor units. Decomposing MMG instead ofEMG increased the number of identifiable motor units by 76%. Notably, MMGexhibits a less pronounced bias to detect superficial motor units.Significance: The presented simulations provide insights into methods to studythe neuromuscular system non-invasively and in vivo that would not be easilyfeasible by other means. Hence, this study provides guidance for thedevelopment of novel biomedical technologies.

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