Reducing Noise, Artifacts and Interference in Single-Channel EMG Signals: A Review

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 6; p. 2927
• Main Authors: Boyer, Marianne, Bouyer, Laurent, Roy, Jean-Sébastien, Campeau-Lecours, Alexandre
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.03.2023; MDPI
• Subjects: Algorithms; Best practice; contaminant reduction; Contaminants; Electrodes; Electromyography; Electromyography - methods; Fatigue; Implants, Artificial; interference; Methods; Muscle Fatigue; Muscular fatigue; noise; Noise reduction; Prostheses; Prosthesis; Review; Robot control; Signal processing; Signal Processing, Computer-Assisted; New York; Germany; artifact; denoising; signal processing; electromyography; contaminant reduction; noise; interference; filtering
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23062927

Electromyography (EMG) is gaining importance in many research and clinical applications, including muscle fatigue detection, control of robotic mechanisms and prostheses, clinical diagnosis of neuromuscular diseases and quantification of force. However, EMG signals can be contaminated by various types of noise, interference and artifacts, leading to potential data misinterpretation. Even assuming best practices, the acquired signal may still contain contaminants. The aim of this paper is to review methods employed to reduce the contamination of single channel EMG signals. Specifically, we focus on methods which enable a full reconstruction of the EMG signal without loss of information. This includes subtraction methods used in the time domain, denoising methods performed after the signal decomposition and hybrid approaches that combine multiple methods. Finally, this paper provides a discussion on the suitability of the individual methods based on the type of contaminant(s) present in the signal and the specific requirements of the application.