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Cited by 233Muscle activation and electromyography studiesECG Monitoring and AnalysisNeuroscience and Neural Engineering

Tutorial. Surface EMG detection, conditioning and pre-processing: Best practices

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Merletti, R., Cerone, G.L.·2020·Journal of Electromyography and Kinesiology
DOI 10.1016/j.jelekin.2020.102440
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Abstract

This tutorial is aimed primarily to non-engineers, using or planning to use surface electromyography (sEMG) as an assessment tool for muscle evaluation in the prevention, monitoring, assessment and rehabilitation fields. The main purpose is to explain basic concepts related to: (a) signal detection (electrodes, electrode-skin interface, noise, ECG and power line interference), (b) basic signal properties, such as amplitude and bandwidth, (c) parameters of the front-end amplifier (input impedance, noise, CMRR, bandwidth, etc.), (d) techniques for interference and artifact reduction, (e) signal filtering, (f) sampling and (g) A/D conversion, These concepts are addressed and discussed, with examples. The second purpose is to outline best practices and provide general guidelines for proper signal detection, conditioning and A/D conversion, aimed to clinical operators and biomedical engineers. Issues related to the sEMG origin and to electrode size, interelectrode distance and location, have been discussed in a previous tutorial. Issues related to signal processing for information extraction will be discussed in a subsequent tutorial.

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  • The frequency range of the input sine wave signals used in the performance tests (10-210 Hz) spans much of the relevant range of EEG, ECG, and EMG signals.

    “Hence, the frequency range of the input sine wave signals used in our performance tests spans much of the relevant range of these three common bioelectric signals.”

    From Comparison between Two Time Synchronization and Data Alignment Methods for Multi-Channel Wearable Biosensor Systems Using BLE Protocol (2023) · cites this paper as [37]

    The paper describes an sEMG bandwidth of 20–500 Hz (Analog low-pass, high-pass and notch filters; page unavailable), which overlaps the citing paper’s 10–210 Hz test range. Our copy does not establish the relevant frequency ranges for EEG or ECG, and 10–210 Hz does not cover the full stated sEMG bandwidth. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Merletti, R., & Cerone, G. L.
Affiliations
Politecnico di Torino (IT)
Corresponding author
Merletti, R.
ORCID
Venue
Journal of Electromyography and Kinesiology
Publisher
Elsevier BV
Published
2020-10
Volume
54
Pages
102440
ISSN
1050-6411 (ISSN-L)
DOI
10.1016/j.jelekin.2020.102440
OpenAlex
W3037535647
Cited by
233
References
1
Citations by year
2026: 46 · 2025: 52 · 2024: 56 · 2023: 37 · 2022: 21 · 2021: 17 · 2020: 4
FWCI
6.21
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Electromyography and Kinesiology (publishedVersion)
  • PubMed (publishedVersion)
  • PORTO@Iris (submittedVersion, cc-by-nc-nd)
Topics
  • Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
  • Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
Keywords
surface electromyography, signal detection, electrodes, skin-electrode interface, ECG interference, power line interference, front-end amplifier, signal filtering, sampling, analog-to-digital conversion, artifact reduction, muscle assessment, best practices
MeSH terms
Amplifiers, Electronic, Electrodes, Electromyography, Humans, Artifacts, Practice Guidelines as Topic, Muscle, Skeletal

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