Being able to decode neural signals that control skeletal muscles with high accuracy will enable scientific breakthroughs in diagnostics and treatment, including early detection of neurodegenerative diseases, optimising personalised treatment or gene therapy, and assistive technologies like neuroprostheses. This breakthrough will require technology that is able to record signals from skeletal muscles in sufficient detail to allow the morpho-functional state of the neuromuscular system to be extracted. No existing technology can do this. Measuring the magnetic field induced by the flow of electrical charges in skeletal muscles, known as Magnetomyography (MMG), is expected to be a game-changing technology because magnetic fields are not attenuated by biological tissue. However, the extremely small magnetic fields involved require extremely sensitive magnetometers. The only promising option is novel quantum sensors, such as optically pumped magnetometers (OPMs), because they are small and modular. Our vision is to use this technology and our expertise in computational neuromechanics to decode, for the first time, neuromuscular control of skeletal muscles based on in vivo, high-density MMG data. For this purpose, we will design the first high-density MMG prototype and develop custom calibration techniques. We will record magnetic fields induced by skeletal muscles and combine them with the advanced computational musculoskeletal system models, which will allow us to derive robust and reliable source localisation and separation algorithms.
Benchmarking experiments: MMG and EMG are complementary modalities for recording the electrical activity of muscles. In contrast to EMG, however, MMG has not been well explored. With the qMOTION project, we investigate whether MMG can provide new insights into neuromuscular physiology that are not accessible by EMG. For this purpose, it is essential to measure MMG and EMG simultaneously at the highest resolution and quality currently possible. This includes special non-magnetic EMG electrodes. Ultimately, we want to record benchmark data sets that compare the performance of MMG and EMG for decoding human motion.
Modelling and simulation: Simulations provide insights into MMG signals that would not be easily possible experimentally. Most importantly, simulations can precisely define muscular activation, tissue properties, and measurement accuracy. These factors usually lead to significant uncertainties in experimental data. In addition, labelled data sets can be generated to test and benchmark signal processing algorithms, such as motor unit decomposition or inverse modelling-based imaging.
Phantom fibre measurements: OPM-MMG is a novel methodology, and its limitations are not well explored. The quantification of data uncertainty during benchmark experiments and well-calibrated sensors is essential for the accuracy of MMG-based biomedical applications. To address this, a device called the "Phantom Fiber" is being developed to simulate the magnetic field of a muscle fiber during contraction to calibrate and verify sensor setups containing multiple OPMs.
