A computational approach for generating continuous estimates of motor unit discharge rates and visualizing population discharge characteristics.

. Successive improvements in high density surface electromyography and decomposition techniques have facilitated an increasing yield in decomposed motor unit (MU) spike times. Though these advancements enhance the generalizability of findings and promote the application of MU discharge characteristics to inform the neural control of motor output, limitations remain. Specifically, (1) common approaches for generating smooth estimates of MU discharge rates introduce artifacts in quantification, which may bias findings, and (2) discharge characteristics of large MU populations are often difficult to visualize.. In the present study, we propose support vector regression (SVR) as an improved approach for generating smooth continuous estimates of discharge rate and compare the fit characteristics of SVR to traditionally used methods, including Hanning window filtering and polynomial regression. Furthermore, we introduce ensembles as a method to visualize the discharge characteristics of large MU populations. We define ensembles as the average discharge profile of a subpopulation of MUs, composed of a time normalized ensemble average of all units within this subpopulation. Analysis was conducted with MUs decomposed from the tibialis anterior (= 2128), medial gastrocnemius (= 2673), and soleus (= 1190) during isometric plantarflexion and dorsiflexion contractions.. Compared to traditional approaches, we found SVR to alleviate commonly observed inaccuracies and produce significantly less absolute fit error in the initial phase of MU discharge and throughout the entire duration of discharge. Additionally, we found the visualization of MU populations as ensembles to intuitively represent population discharge characteristics with appropriate accuracy for visualization.. The results and methods outlined here provide an improved method for generating estimates of MU discharge rate with SVR and present a unique approach to visualizing MU populations with ensembles. In combination, the use of SVR and generation of ensembles represent an efficient method for rendering population discharge characteristics.