Cited by 400Muscle activation and electromyography studiesPeripheral Nerve DisordersMuscle Physiology and Disorders

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Accurate identification of motor unit discharge patterns from high-density surface EMG and validation with a novel signal-based performance metric.

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Holobar, A, Minetto, M A, Farina, D·2014·Journal of Neural Engineering

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Abstract

OBJECTIVE: A signal-based metric for assessment of accuracy of motor unit (MU) identification from high-density surface electromyograms (EMG) is introduced. This metric, so-called pulse-to-noise-ratio (PNR), is computationally efficient, does not require any additional experimental costs and can be applied to every MU that is identified by the previously developed convolution kernel compensation technique. APPROACH: The analytical derivation of the newly introduced metric is provided, along with its extensive experimental validation on both synthetic and experimental surface EMG signals with signal-to-noise ratios ranging from 0 to 20 dB and muscle contraction forces from 5% to 70% of the maximum voluntary contraction. MAIN RESULTS: In all the experimental and simulated signals, the newly introduced metric correlated significantly with both sensitivity and false alarm rate in identification of MU discharges. Practically all the MUs with PNR > 30 dB exhibited sensitivity >90% and false alarm rates <2%. Therefore, a threshold of 30 dB in PNR can be used as a simple method for selecting only reliably decomposed units. SIGNIFICANCE: The newly introduced metric is considered a robust and reliable indicator of accuracy of MU identification. The study also shows that high-density surface EMG can be reliably decomposed at contraction forces as high as 70% of the maximum.

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Authors: Holobar, A., Minetto, M. A., & Farina, D.
Affiliations: University of Maribor (SI)
University of Turin (IT)
Bernstein Center for Computational Neuroscience Göttingen (DE)
Corresponding author: Holobar, A
Minetto, M A
Farina, D
ORCID: Holobar, A
Minetto, M A
Farina, D
Venue: Journal of Neural Engineering
Publisher: IOP Publishing
Published: 2014-01-08
Volume: 11
Issue: 1
Pages: 016008
ISSN: 1741-2552 (ISSN-L)
1741-2560
DOI: 10.1088/1741-2560/11/1/016008
OpenAlex: W2042861111
Cited by: 400
References: 31
Citations by year: 2026: 33 · 2025: 56 · 2024: 48 · 2023: 52 · 2022: 41 · 2021: 40 · 2020: 29 · 2019: 28 · 2018: 20 · 2017: 15 · 2016: 16 · 2015: 9 · 2014: 13
FWCI: 9.17
Type: journal article
Language: en
Open access: green
dk.um.si
Licence: other-oa
iopscience.iop.org/info/page/text-and-data-mining (tdm)
iopscience.iop.org/page/copyright (vor)
Locations: Journal of Neural Engineering (publishedVersion)
PubMed (publishedVersion)
University of Maribor digital library (University of Maribor) (publishedVersion, other-oa)
GRO (submittedVersion)
Topics: Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
Health Sciences › Medicine › Clinical Neurology › Peripheral Nerve Disorders
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Muscle Physiology and Disorders
Keywords: motor unit identification, high-density surface electromyography, motor unit firing patterns, signal-to-noise ratio, muscle contraction force, motor unit decomposition
MeSH terms: Algorithms, Data Interpretation, Statistical, Electromyography, Humans, Isometric Contraction, Motor Neurons, Muscle Contraction, Signal Processing, Computer-Assisted, Reproducibility of Results, Linear Models, Muscle, Skeletal, Muscle Fibers, Skeletal, Signal-To-Noise Ratio

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Accurate identification of motor unit discharge patterns from high-density surface EMG and validation with a novel signal-based performance metric.

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