Loughborough University
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Neural decoding from surface high-density EMG signals: influence of anatomy and synchronization on the number of identified motor units

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journal contribution
posted on 2022-10-26, 08:45 authored by Daniela Souza de Oliveira, Andrea Casolo, Tom BalshawTom Balshaw, Sumiaki Maeo, Marcel Bahia Lanza, Neil MartinNeil Martin, Nicola Maffulli, Thomas Mehari Kinfe, Bjoern M Eskofier, Jonathan FollandJonathan Folland, Dario Farina, Alessandro Del Vecchio

Objective. High-density surface electromyography (HD-sEMG) allows the reliable identification of individual motor unit (MU) action potentials. Despite the accuracy in decomposition, there is a large variability in the number of identified MUs across individuals and exerted forces. Here we present a systematic investigation of the anatomical and neural factors that determine this variability. Approach. We investigated factors of influence on HD-sEMG decomposition, such as synchronization of MU discharges, distribution of MU territories, muscle-electrode distance (MED—subcutaneous adipose tissue thickness), maximum anatomical cross-sectional area (ACSAmax), and fiber cross-sectional area. For this purpose, we recorded HD-sEMG signals, ultrasound and magnetic resonance images, and took a muscle biopsy from the biceps brachii muscle from 30 male participants drawn from two groups to ensure variability within the factors—untrained-controls (UT = 14) and strength-trained individuals (ST = 16). Participants performed isometric ramp contractions with elbow flexors (at 15%, 35%, 50% and 70% maximum voluntary torque—MVT). We assessed the correlation between the number of accurately detected MUs by HD-sEMG decomposition and each measured parameter, for each target force level. Multiple regression analysis was then applied. Main results. ST subjects showed lower MED (UT = 5.1 ± 1.4 mm; ST = 3.8 ± 0.8 mm) and a greater number of identified MUs (UT: 21.3 ± 10.2 vs ST: 29.2 ± 11.8 MUs/subject across all force levels). The entire cohort showed a negative correlation between MED and the number of identified MUs at low forces (r = −0.6, p = 0.002 at 15% MVT). Moreover, the number of identified MUs was positively correlated to the distribution of MU territories (r = 0.56, p = 0.01) and ACSAmax (r = 0.48, p = 0.03) at 15% MVT. By accounting for all anatomical parameters, we were able to partly predict the number of decomposed MUs at low but not at high forces. Significance. Our results confirmed the influence of subcutaneous tissue on the quality of HD-sEMG signals and demonstrated that MU spatial distribution and ACSAmax are also relevant parameters of influence for current decomposition algorithms.


d.hip (Digital Health Innovation Platform), a cooperation between Siemens Healthineers, Medical Valley, University Hospital Erlangen, and Friedrich-Alexander University (DSO and AdV)

European Research Council Synergy Grant NaturalBionicS (Contract #810346)


Engineering and Physical Sciences Research Council

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Central nervous system and skeletal muscle adaptation associated with strength training

Japan Society for the Promotion of Science

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  • Sport, Exercise and Health Sciences

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Journal of Neural Engineering






IOP Publishing


  • AM (Accepted Manuscript)

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© IOP Publishing

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This is the Accepted Manuscript version of an article accepted for publication in Journal of Neural Engineering. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at https://doi.org/10.1088/1741-2552/ac823d.

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Prof Jonathan Folland. Deposit date: 25 October 2022

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