Investigating the spatial resolution of EMG and MMG based on a systemic multi-scale model

Abstract

While electromyography (EMG) and magnetomyography (MMG) are both methods to measure the electrical activity of skeletal muscles, no systematic comparison between both signals exists. Within this work, we propose a novel in silico model for EMG and MMG and test the hypothesis that MMG surpasses EMG in terms of spatial selectivity, i.e. the ability to distinguish spatially shifted sources. The results show that MMG provides a slightly better spatial selectivity than EMG when recorded directly on the muscle surface. However, there is a remarkable difference in spatial selectivity for non-invasive surface measurements. The spatial selectivity of the MMG components aligned with the muscle fibres and normal to the body surface outperforms the spatial selectivity of surface EMG. Particularly, for the MMG’s normal-to-the-surface component the influence of subcutaneous fat is minimal. Further, for the first time, we analyse the contribution of different structural components, i.e. muscle fibres from different motor units and the extracellular space, to the measurable biomagnetic field. Notably, the simulations show that for the normal-to-the-surface MMG component, the contribution from volume currents in the extracellular space and in surrounding inactive tissues, is negligible. Further, our model predicts a surprisingly high contribution of the passive muscle fibres to the observable magnetic field.

Wearable super-resolution muscle–machine interfacing

“…If a small number of sensing units are used to record muscle activity in a limited area, the final measurement may be over-pronounced by the activity locally. The high spatial selectivity might also bring the problem that it is easily affected by motion artifacts and the offset between the sensor and recording location ( Klotz et al, 2022 ).…”

Optimizing NV magnetometry for Magnetoneurography and Magnetomyography applications

“…Unlike electrical potentials, magnetic fields can be propagated through the body without distortion (Malmivuo et al, 1995;Oschman, 2002). A recent simulation study (Klotz et al, 2022) shows that MMG signals are less affected by subcutaneous fat than a corresponding EMG signal. Accordingly, non-invasive MMG is superior to EMG for distinguishing spatially shifted muscle fiber sources; something highly desirable, for example, for decomposing an interference signal into the spike trains of individual motor units (e.g., Holobar and Zazula, 2007;De Luca et al, 2015;Negro et al, 2016) and for detecting hallmarks of neuromuscular disorders (e.g., Rubin, 2019;Marquetand et al, 2021).…”

“…Figure 7 showcases that differential recording allows to further increase in the spatial sensitivity of MMG measurements. Therefore, using a computational model (Klotz et al, 2022 ), muscle contraction is simulated by selectively stimulating muscle fibers in different depths. The virtual MMG is sampled midway between the innervation zone and the myotendinous junction on the muscle surface and at a distance of from the muscle surface.…”

High-density magnetomyography is superior over surface electromyography for the decomposition of motor units: a simulation study

“…To simulate MUEPs and MUMFs for a population of virtual muscles, a bio-physical model is required. Within this work, we use the multi-domain simulation framework proposed in Klotz et al (2020Klotz et al ( , 2022 to simulate a library of MUEPs and MUMFs. In summary, this multi-scale model follows a bottomup approach, that consistently integrates a microscale description of the transmembrane currents across the muscle fibre membranes and the most important structural properties of skeletal muscle tissue into a multiphase continuum model at the macroscale.…”

“…e.g., Boto et al, 2017, Murzin et al, 2020, Zuo et al, 2020 that it is now possible to explore the use of MMG for biomedical applications (e.g., Broser et al, 2018, Llinás et al, 2020. Therefore, it is essential to support empirical observations from experiments with a solid theoretical understanding of MMG signals (e.g., Klotz et al, 2022).…”