Quantitative and Qualitative Parameters of Myoelectric Signals for Computational Simulations of Human Surface Electromyograms

Freitas, Roberto M. de; Kohn, André Fábio

doi:10.1145/3397391.3397446

Cited by 1 publication

(8 citation statements)

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“…Surface MUAP waveforms were detected and estimated from intramuscular and surface EMG signals using EMGLAB software [ 24 , 34 , 37 , 38 ]. Specifically, the time instant of peak amplitude of intramuscular and surface MUAPs were automatically identified from EMG signals recorded during protocol 1 and protocol 2 (see Section 2.3.1) using EMGLAB [ 34 ].…”

Section: Methodsmentioning

confidence: 99%

“…Rigorous manual corrections were performed to group MUAPs with the same amplitude, number of phases and duration. Moreover only MUAPs clearly standing out of the baseline (“noise background”) were considered [ 24 , 25 ]. Surface MUAP waveforms were estimated by averaging time-windows of the surface EMG signals at the time instant of peak amplitude of intramuscular or surface MUAPs, such as illustrated in Fig 2 [ 24 , 25 , 39 ].…”

Section: Methodsmentioning

confidence: 99%

“…Moreover only MUAPs clearly standing out of the baseline (“noise background”) were considered [ 24 , 25 ]. Surface MUAP waveforms were estimated by averaging time-windows of the surface EMG signals at the time instant of peak amplitude of intramuscular or surface MUAPs, such as illustrated in Fig 2 [ 24 , 25 , 39 ]. Each MUAP waveform was estimated from at least 100 time-windows, which had a 75 ms duration.…”

Section: Methodsmentioning

confidence: 99%

“…In total, 294 surface MUAP waveforms were estimated (SO: 101, MG: 75, LG: 69, and TA: 49). This dataset of surface MUAP waveforms has been previously used in [ 24 ], and it is currently available for use in ( https://doi.org/10.17605/OSF.IO/MXTQG ).…”

Section: Methodsmentioning

confidence: 99%

“…Characteristics of myoelectric signals (i.e., surface MUAP and EMG interference pattern) may be critically affected by electrode configurations used during recordings [ 4 , 9 , 16 , 17 , 23 ], as well as electrical properties and anatomy of muscle fibers [ 24 – 28 ]. For instance, Farina has shown that surface MUAP waveforms with longer durations are acquired from muscle fibers with longer lengths [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

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Spectral characterization of human leg EMG signals from an open access dataset for the development of computational models

de Freitas,

Kohn

2024

PLoS ONE

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Large-scale neuromusculoskeletal models have been used for predicting mechanisms underlying neuromuscular functions in humans. Simulations of such models provide several types of signals of practical interest, such as surface electromyographic signals (EMG), which are compared with experimental data for interpretations of neurophysiological phenomena under study. Specifically, realistic characterization of spectral properties of simulated EMG signals is important for achieving powerful inferences, whereas considerations should be taken for myoelectric signals of different muscles. In this study, we characterized spectral properties of surface interference pattern EMG signals and motor unit action potentials (MUAP) acquired from three plantar flexor muscles: Soleus (SO), Medial Gastrocnemius (MG), and Lateral Gastrocnemius (LG); and one dorsiflexor muscle: Tibialis Anterior (TA). Surface EMG signals were acquired from 20 participants using the same convention for electrode placement. Specifically, interference pattern EMG signals were obtained during isometric constant force contractions at 5%, 10% and 20% of maximum voluntary contraction (MVC), whereas surface MUAPs were decomposed from surface EMG signals obtained at low contraction forces. We compared the spectrum median frequency (MDF) estimated from interference pattern EMG signals across muscles and contraction intensities. Additionally, we compared MDF and durations of MUAPs between muscles. Our results showed that MDF of interference pattern EMG signals acquired from TA were higher compared to SO, MG, and LG for all contraction intensities i.e., 5%, 10%, and 20% MVC. Consistently, MUAPs acquired from TA also had higher MDF values and shorter durations compared to the other leg muscles. We provide herein a dataset with the surface MUAPs waveforms and interference pattern EMG signals obtained for this study, which should be useful for implementing and validating the simulation of myoelectrical signals of leg muscles. Importantly, these results indicate that spectral properties of myoelectrical signals should be considered for improving EMG modeling in large-scale neuromusculoskeletal models.

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