Decomposing motor units through elimination for real-time intention driven assistive neurotechnology
Nicholas Tacca, Bryan Schlink, Jackson Levine, Mary Heimann, Collin Dunlap, Sam Colachis, Philip Putnam, Matthew Zeglen, Daniel Brobston, Austin Bollinger, José Pons, Lauren Wengerd, Eric Meyers, David Friedenberg
Extracting neural signals at the single motor neuron level provides an optimal control signal for neuroprosthetic applications. However, current algorithms to decompose motor units from high-density electromyography (HD-EMG) are time-consuming and inconsistent, limiting their application to controlled scenarios in a research setting. We introduce MUelim, an algorithm for efficient motor unit decomposition that uses approximate joint diagonalization with a subtractive approach to rapidly identify and refine candidate sources. The algorithm incorporates an extend-lag procedure to augment data for enhanced source separability prior to diagonalization. By systematically iterating and eliminating redundant or noisy sources, MUelim achieves high decomposition accuracy while significantly reducing computational complexity, making it well-suited for real-time applications. We validate MUelim by demonstrating its ability to extract motor units in both simulated and physiological HD-EMG grid data. We benchmark its performance against existing state-of-the-art decomposition algorithms, demonstrating up to a $\sim$3,700\% speed increase to decompose a similar number of sources with a high signal-to-noise ratio. Furthermore, we showcase a real-world application of MUelim in a clinical setting in which an individual with spinal cord injury controlled an EMG-driven neuroprosthetic to perform functional tasks. We demonstrate the ability to decode motor intent in real-time using a spiking neural network trained on the decomposed motor unit spike trains to trigger functional electrical stimulation patterns that evoke hand movements during task practice therapy. We show that the use of motor units enables nuanced motor control, highlighting the potential of MUelim to advance assistive neurotechnology and rehabilitation by enabling precise, closed-loop motor-unit-driven control to the user.