This dataset contains simulated high-density magnetomyography data and high-desnity surface elecoromyography data that was generated for the publication High-density magnetomyography is superior to high-density surface electromyography for motor unit decomposition: a simulation study.

Abstract: Objective: Studying motor units (MUs) 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 in vivo 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 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. Approach: In this work, we perform in silico 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. Main results: 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. Significance: The presented simulations provide insights into methods to study the neuromuscular system non-invasively and in vivo that would not be easily feasible by other means. Hence, this study provides guidance for the development of novel biomedical technologies.

Instructions: This data repository is structured as follows:

The folder 'motor_unit_responses' contains the simulated motor unit electric potentials and motor unit magnetic fields. The folder 'mvc_experiments' contains the simulated HD-sEMG and HD-MMG signals of the simulated voluntary isometric contractions. The folder 'decomposition_results' contains the output of the motor unit decompositions. The folder 'simulation_files' contains the source code required to perform the presented in-silico experiments. The folder 'in-silico_decomposition_source_code' contains the source code required to perform the presented motor unit decompositions. The folder 'make_figures' contains the source code required to replicate the presented Figures.

The data presented in the manuscript can be replicated with the following steps:

Simulating the motor unit responses requires to download a freely available Matlab simulation environment (https://bitbucket.org/klotz_t/multi_domain_fd_code/). An input file (simulate_motor_unit_response_library.m) to run the specific simulations is provided in this dataset. Performing the in-silico MVC experiments requires to run the script 'compute_interference_signal.m'. The in-silcio motor unit decomposition can be performed by executing the script 'in_silico_trials.m'.

Matlab, 2021a