Simulation of motor unit action potential recordings from intramuscular multichannel scanning electrodes
File(s)
Published version
Author(s)
Konstantin, Akhmadeev
Yu, Tianyi
Le Carpentier, Eric
Aoustin, Yannick
Farina, Dario
Type
Journal Article
Abstract
Multi-channel intramuscular EMG (iEMG) provides information on motor neuron behavior, muscle fiber (MF) innervation geometry and, recently, has been proposed as a means to establish a human-machine interface. Objective: to provide a reliable benchmark for computational methods applied to such recordings, we propose a simulation model for iEMG signals acquired by intramuscular multi-channel electrodes. Methods: we propose several modifications to the existing motor unit action potentials (MUAPs) simulation methods, such as farthest point sampling (FPS) for the distribution of motor unit territory centers in the muscle cross-section, accurate fiber-neuron assignment algorithm, modeling of motor neuron action potential propagation delay, and a model of multi-channel scanning electrode. Results: we provide representative applications of this model to the estimation of motor unit territories and the iEMG decomposition evaluation. Also, we extend it to a full multi-channel iEMG simulator using classic linear EMG modeling. Conclusions: altogether, the proposed models provide accurate MUAPs across the entire motor unit territories and for various electrode configurations. Significance: they can be used for the development and evaluation of mathematical methods for multi-channel iEMG processing and analysis.
Date Issued
2020-07-01
Date Acceptance
2019-11-02
Citation
IEEE Transactions on Biomedical Engineering, 2020, 67 (7), pp.2005-2014
ISSN
0018-9294
Publisher
Institute of Electrical and Electronics Engineers
Start Page
2005
End Page
2014
Journal / Book Title
IEEE Transactions on Biomedical Engineering
Volume
67
Issue
7
Copyright Statement
© 2019 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/
License URL
Sponsor
Commission of the European Communities
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000544063000018&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Grant Number
810346
Subjects
Science & Technology
Technology
Engineering, Biomedical
Engineering
Muscles
Manganese
Electrodes
Action potentials
Electromyography
Computational modeling
Neurons
EMG modeling
multi-channel EMG
motor unit modeling
farthest point sampling
NEURAL DRIVE
EMG SIGNALS
FIBER TYPES
DECOMPOSITION
MUSCLES
MODEL
Publication Status
Published
Date Publish Online
2019-12-06