A high-density electromyography system for non-invasive functional motor unit characterization

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Surface electromyography (sEMG) is a non-invasive technique that utilizes surface electrodes placed on the skin to record underlying electrical potentials that drive muscle contraction. sEMG has been integral to our understanding of human movement and has important applications in many fields, including robotics, medicine, rehabilitation and ergonomics. Significant advances in EMG sampling and analysis have recently been made, including the bundling of electrodes into high-density grids to exponentially increase the volume of the EMG detection zone to extract a more refined representation of the overall neural drive to a muscle. As such, this RTI proposal is requesting funds for a versatile 384-channel bioelectrical amplifier capable of simultaneously sampling 64-channels of high-density surface EMG (HD-EMG), intramuscular EMG, electroencephalography, force and other biomedical signals central to our respective research programs. This system interfaces with open-source software with sophisticated algorithms that can automatically classify the individual motor unit action potentials that summate to form the sEMG signal. Until recently, this type of motor unit decomposition was only possible using invasive intramuscular EMG, which was not well tolerated by participants and required laborious manual signal processing to isolate the individual motor unit action potentials that comprised the global EMG signal. This equipment will replace essential failing sEMG systems, which has caused tremendous strain and delays on our otherwise productive research programs. Importantly, this infrastructure upgrade will also allow us to remain competitive and on the cutting edge of our respective fields, as HD-EMG recordings and motor unit decomposition are now essential to conduct high-quality neuromuscular research. Within our collaborative Ontario Tech Neuromechanics Research Group, HD-EMG and motor unit decomposition will be used to investigate changes in the neural control of upper limb muscles in response to both experimental perturbations and chronic neuromuscular conditions, and to validate and further develop computational models of motor unit fatigue and recovery for proactive ergonomics work simulation analyses. These types of functional outcomes were not previously possible, and represent an incredible opportunity for our HQP to engage with both fundamental and applied research with high impact. As far as we are aware, our research group would be the only regional users of this HD-EMG technology within 150 km of our institution. As such, we anticipate numerous collaborations with Faculty members within and outside our university community, and have outlined a detailed training and management plan to ensure optimized operation. We welcome these opportunities to further leverage this equipment to make important advances in our field and to provide the best possible training to HQP working with this cutting-edge technology.