Throughout the history of neuroscience, the understanding of human movement has depended, in part, on our ability to measure the firings of α motoneurons, the final common pathway of inputs from the central nervous system to the muscle fibers they innervate. The study of these anatomical pathways, or motor units (MUs), and their resulting action potentials (MUAPs) in the electromyographic (EMG) signal is a well-established means of interrogating the structure and function of the neuromuscular system. Historically, the process of identifying MUAPs – whether visually or by means of automated algorithms – has generally been limited to low force isometric contractions and offline processing, due to the computational and algorithmic intractability of processing dynamic surface EMG signals, which are nonstationary and contain larger numbers of MUs [1].