Evolution of surface electromyography: from muscle electrophysiology towards neural recording and interfacing
File(s)FarinaEnoka_JEK_Revision1_Clean.pdf (917.46 KB)
Accepted version
Author(s)
Farina, Dario
Enoka, Roger M
Type
Journal Article
Abstract
Surface electromyography (EMG) comprises a recording of electrical activity from the body surface generated by muscle fibres during muscle contractions. Its characteristics depend on the fibre membrane potentials and the neural activation signal sent from the motor neurons to the muscles. EMG has been classically used as the primary investigation tool in kinesiology studies in a variety of applications. More recently, surface EMG techniques have evolved from single-channel methods to high-density systems with hundreds of electrodes. High-density EMG recordings can be deconvolved to estimate the discharge times of spinal motor neurons innervating the recorded muscles, with algorithms that have been developed and validated in the last two decades. Within limits and with some variability across muscles, these techniques provide a non-invasive method to study relatively large populations of motor neurons in humans. Surface EMG is thus evolving from a peripheral measure of muscle electrical activity towards a neural recording and neural interfacing signal. These advances in technology have had a major impact on our fundamental understanding of the neural control of movement and have exposed new perspectives in neurotechnologies. Here we provide an overview and perspective of modern EMG technology, as derived from past achievements, and its impact in neurophysiology and neural engineering.
Date Issued
2023-08
Date Acceptance
2023-06-01
Citation
Journal of Electromyography and Kinesiology, 2023, 71, pp.1-8
ISSN
1050-6411
Publisher
Elsevier
Start Page
1
End Page
8
Journal / Book Title
Journal of Electromyography and Kinesiology
Volume
71
Copyright Statement
Copyright © Elsevier Ltd. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
Identifier
https://www.sciencedirect.com/science/article/pii/S105064112300055X
Publication Status
Published
Article Number
102796
Date Publish Online
2023-06-01