Bioelectromagnetic fields for studying neuromuscular physiology : in silico investigations of EMG and MMG

Abstract

Skeletal muscles generate bioelectromagnetic fields that contain information about the neural control of motions and the function of the muscle. One distinguishes between electromyography (EMG), the measurement of the muscle-induced electric potential field, and magnetomyography (MMG), the recording of muscle-induced magnetic fields. EMG is a well-established methodology, and its limitations have been extensively discussed in the scientific literature. In contrast, MMG is an emerging methodology with the potential to overcome some of the inherent limitations of EMG. To unlock the full potential of MMG, it is essential to support empirical observations from experiments with a solid theoretical understanding of muscle-induced bioelectromagnetic fields. Therefore, this thesis derives a novel multiscale skeletal muscle model that can predict realistic EMG and MMG signals. This model is used to conduct the first systematic comparison between surface EMG and non-invasive MMG. By using simulations, all system parameters can be controlled precisely. This would not be possible experimentally. The fundamental properties of EMG and MMG are systematically explored using simulations comparable to electrically or reflex-evoked contractions. Notably, it is shown that non-invasive MMG data is spatially more selective than comparable high-density EMG data. This property, for example, is advantageous for decomposing signals of voluntary contractions into individual motor unit spike trains. Using a novel in silico trial framework, it is demonstrated that non-invasive MMG-based motor unit decomposition is superior to the well-established surface EMG-based motor unit decomposition.