Tutorial on MUedit: an open-source software for identifying and analysing the discharge timing of motor units from electromyographic signals
File(s)
Accepted version
Author(s)
Avrillon, Simon
Hug, Francois
Baker, Stuart N
Gibbs, Ciara
Farina, Dario
Type
Journal Article
Abstract
We introduce the open-source software MUedit and we describe its use for identifying the discharge timing of motor units from all types of electromyographic (EMG) signals recorded with multi-channel systems. MUedit performs EMG decomposition using a blind-source separation approach. Following this, users can display the estimated motor unit pulse trains and inspect the accuracy of the automatic detection of discharge times. When necessary, users can correct the automatic detection of discharge times and recalculate the motor unit pulse train with an updated separation vector. Here, we provide an open-source software and a tutorial that guides the user through (i) the parameters and steps of the decomposition algorithm, and (ii) the manual editing of motor unit pulse trains. Further, we provide simulated and experimental EMG signals recorded with grids of surface electrodes and intramuscular electrode arrays to benchmark the performance of MUedit. Finally, we discuss advantages and limitations of the blind-source separation approach for the study of motor unit behaviour during tonic muscle contractions.
Date Issued
2024-08-01
Date Acceptance
2024-05-01
Citation
Journal of Electromyography and Kinesiology, 2024, 77
ISSN
1050-6411
Publisher
Elsevier
Journal / Book Title
Journal of Electromyography and Kinesiology
Volume
77
Copyright Statement
Copyright © 2024 Published by Elsevier Ltd. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/38761514
PII: S1050-6411(24)00030-0
Subjects
ACTION-POTENTIALS
DECOMPOSITION
ELECTRODE
EXTRACTION
IDENTIFICATION
Life Sciences & Biomedicine
Neurosciences
Neurosciences & Neurology
Physiology
PHYSIOLOGY
RECORDINGS
REFLEX
Rehabilitation
Science & Technology
Sport Sciences
Publication Status
Published
Coverage Spatial
England
Article Number
102886
Date Publish Online
2024-05-13