A Review of Techniques for Surface Electromyography Signal Quality Analysis

Electromyography (EMG) signals are instrumental in a variety of applications including prosthetic control, muscle health assessment, rehabilitation, and workplace monitoring. Signal contaminants including noise, interference, and artifacts can degrade the quality of the EMG signal, leading to misint...

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Bibliographic Details
Published in:IEEE reviews in biomedical engineering 2023, Vol.16, p.472-486
Main Authors: Farago, Emma, MacIsaac, Dawn, Suk, Michelle, Chan, Adrian D. C.
Format: Article
Language:English
Subjects:
Algorithms
Biomedical signal processing
Contaminants
Data analysis
Data collection
Electrodes
Electromyography
Electromyography - methods
Filtering
high-density EMG
Humans
Interference
Monitoring
Muscle Contraction - physiology
Muscle, Skeletal
Muscles
Noise monitoring
Noise pollution
Outliers (statistics)
Pollution measurement
Pollution monitoring
Prostheses
Rehabilitation
Signal monitoring
Signal Processing, Computer-Assisted
Signal quality
signal quality analysis
surface EMG
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Summary:Electromyography (EMG) signals are instrumental in a variety of applications including prosthetic control, muscle health assessment, rehabilitation, and workplace monitoring. Signal contaminants including noise, interference, and artifacts can degrade the quality of the EMG signal, leading to misinterpretation; therefore it is important to ensure that collected EMG signals are of sufficient quality prior to further analysis. A literature search was conducted to identify current approaches for detecting, identifying, and quantifying contaminants within surface EMG signals. We identified two main strategies: 1) bottom-up approaches for identifying specific and well-characterized contaminants and 2) top-down approaches for detecting anomalous EMG signals or outlier channels in high-density EMG arrays. The best type(s) of approach are dependent on the circumstances of data collection including the environment, the susceptibility of the application to contaminants, and the resilience of the application to contaminants. Further research is needed for assessing EMG with multiple simultaneous contaminants, identifying ground-truths for clean EMG data, and developing user-friendly and autonomous methods for EMG signal quality analysis.
ISSN:1937-3333
1941-1189
DOI:10.1109/RBME.2022.3164797