@article{de:2022:1741-2552/ac823d,
author = {de, Oliveira DS and Casolo, A and Balshaw, TG and Maeo, S and Lanza, MB and Martin, NRW and Maffulli, N and Kinfe, TM and Eskofier, BM and Folland, JP and Farina, D and Del, Vecchio A},
doi = {1741-2552/ac823d},
journal = {Journal of Neural Engineering},
title = {Neural decoding from surface high-density EMG signals: influence of anatomy and synchronization on the number of identified motor units},
url = {http://dx.doi.org/10.1088/1741-2552/ac823d},
volume = {19},
year = {2022}
}

Download

TY  - JOUR
AB  - 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 l
AU  - de,Oliveira DS
AU  - Casolo,A
AU  - Balshaw,TG
AU  - Maeo,S
AU  - Lanza,MB
AU  - Martin,NRW
AU  - Maffulli,N
AU  - Kinfe,TM
AU  - Eskofier,BM
AU  - Folland,JP
AU  - Farina,D
AU  - Del,Vecchio A
DO  - 1741-2552/ac823d
PY  - 2022///
SN  - 1741-2560
TI  - Neural decoding from surface high-density EMG signals: influence of anatomy and synchronization on the number of identified motor units
T2  - Journal of Neural Engineering
UR  - http://dx.doi.org/10.1088/1741-2552/ac823d
UR  - https://iopscience.iop.org/article/10.1088/1741-2552/ac823d
VL  - 19
ER  - 

Download