Adaptive motor unit decomposition using a cross-validation-based update policy.

Extraction of motor unit (MU) information from electromyographic (EMG) signals has shown promise in neurophysiology and rehabilitation. However, the low accuracy of MU spike train firing information remains a major issue when the signals have stochastic variations. The objective of this study was to develop a new adaptive MU spike train decomposition algorithm with a deterministic pool of MU spike trains update policy. We first identified common MU spike trains, which were proven to be accurate, from two groups of concurrently recorded EMG signals. We then updated the common pool of MU spike trains with a flag policy, when we periodically updated the MU spike train separation matrix, which could add newly identified MU spike trains and remove inaccurate MU spike trains from the MU spike train pool. The flags of individual MU spike trains captured the consistency of MU active state and the likelihood of being extracted by the decomposition algorithm repetitively. We systematically evaluated the new algorithm on simulated datasets with 1-h pseudorandom activation levels under various conditions, including different degrees of amplitude drift of action potentials, different rates of MU rotation, and different levels of signal-to-noise ratios. The results demonstrated that our adaptive algorithm could identify and retain MU spike trains with 28 % higher accuracy compared with the conventional decomposition method. We also found consistently high decomposition accuracy across various signal conditions. These findings highlight the robustness of our decomposition approach. The outcomes have the potential to enhance neural decoding performance and could be applied to different scenarios, such as evaluating neurophysiological mechanisms during sustained muscle activations and assessing motor recovery during rehabilitation.