Motor unit filter prelearning strategies for decomposition of compound muscle action potentials in high-density...

We analyze the efficiency of motor unit (MU) filter prelearning from high-density surface electromyographic (HDEMG) recordings of voluntary muscle contractions in the identification of the motor unit firing patterns during elicited muscle contractions. Motor unit filters are assessed from 10 s long low level isometric voluntary contractions by gradient-based optimization of three different cost functions and then applied to synthetic HDEMG recordings of elicited muscle contractions with dispersion of motor unit firings ranging from 13 ms to 1 ms. We demonstrate that the number of identified MUs and the precision of MU identification depend significantly on the selected cost function. Regardless the selected cost function and MU synchronization level, the median precision of motor unit identification in elicited contraction is ≥ 95 % and is comparable to the precision in voluntary contractions. On the other hand, median miss rate increases significantly from < 1 % to ~ 3 % with the tested level of MU synchronization.Clinical Relevance-The identification of MU firings from HDEMG in elicited muscle contractions provides a new tool for in vivo investigation of motor excitability in humans.