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

Souza de Oliveira D, Casolo A, Balshaw TG, Maeo S, Lanza MB, Martin NRW, Maffulli N, Kinfe TM, Eskofier B, Folland JP, Farina D, Del Vecchio A (2022)

Publication Type: Journal article

Publication year: 2022

Journal

Book Volume: 19

Journal Issue: 4

DOI: 10.1088/1741-2552/ac823d

Abstract

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 ACSAmaxare also relevant parameters of influence for current decomposition algorithms.

Authors with CRIS profile

Involved external institutions

Loughborough University United Kingdom (GB) Imperial College London / The Imperial College of Science, Technology and Medicine United Kingdom (GB) University of Padua / Universita degli Studi di Padova Italy (IT) Università degli Studi di Salerno Italy (IT)

How to cite

APA:

Souza de Oliveira, D., Casolo, A., Balshaw, T.G., Maeo, S., Lanza, M.B., Martin, N.R.W.,... Del Vecchio, A. (2022). Neural decoding from surface high-density EMG signals: influence of anatomy and synchronization on the number of identified motor units. Journal of Neural Engineering, 19(4). https://doi.org/10.1088/1741-2552/ac823d

MLA:

Souza de Oliveira, Daniela, et al. "Neural decoding from surface high-density EMG signals: influence of anatomy and synchronization on the number of identified motor units." Journal of Neural Engineering 19.4 (2022).

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