ReviewSensors (Basel, Switzerland)2023
Reducing Noise, Artifacts and Interference in Single-Channel EMG Signals: A Review.
Review in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
42 citing papers in PubMed, 133 citations in OpenAlex.
- Optimizing Session Frequency in EEG Biofeedback: A Comparative Study of Protocol Dynamics and Neuromuscular Adaptation in Elite Judo Athletes.Sensors (Basel, Switzerland) · 2026Trial
- Study on mental fatigue detection of crane operators based on frequency and channel attention spiking neural network with multivariate variational mode decomposition in real construction environments.Medical & biological engineering & computing · 2026Article
- Motion Neural Monitoring Based on Flexible Materials: From Signal Acquisition to Training Enhancement.ACS omega · 2026Review
- Sensor-Based Classification of Post-Stroke Motor Impairment Using Fugl-Meyer Lower Extremity Scores.Sensors (Basel, Switzerland) · 2026Article
- Advances in the Interpretation of the Electrocardiogram by Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2026Review
- Feature-Level Fusion of Surface Electromyography and Mechanomyography Signals for MVC-Normalized Shoulder Abduction Force-Level Classification in Healthy Adults.Sensors (Basel, Switzerland) · 2026Article
- Wearable Sensing for Clinical Physiology Monitoring: Emerging Paradigms.Physiology (Bethesda, Md.) · 2026Review
- VisR Ultrasound for Monitoring Muscle Fatigue with FES-Induced and Volitional Isometric Contraction of the Rectus Femoris in Participants with Uninjured and Injured Spinal Cords.Annals of biomedical engineering · 2026Article
- MoCap-Referenced Neck-Shoulder sEMG-IMU Decoding for Discrete Assistive Commands: A Pilot Study.Sensors (Basel, Switzerland) · 2026Article
- sEMG-Based Motion Intention Recognition for Interactive Upper Limb Nursing Assistance.Sensors (Basel, Switzerland) · 2026Article
- Assessment of the severity of lower extremity peripheral arterial disease based on gastrocnemius surface electromyography and mechanomyography signals.Medical & biological engineering & computing · 2026Article
- EEG and IMU Gait Signal Processing: A Comparative Assessment of the "Reza" Exponential Filter and Classical Filters.Sensors (Basel, Switzerland) · 2026Article
- The Body Knows What the Mind Does Not: Uncertainty Affects Physiological Markers of Deception.Psychophysiology · 2026Article
- Smart Devices and Multimodal Systems for Mental Health Monitoring: From Theory to Application.Bioengineering (Basel, Switzerland) · 2026Review
- Review
- Continuous wavelet transform-based analysis of shoulder muscle fatigue during dynamic abduction: a surface electromyography study.Frontiers in bioengineering and biotechnology · 2026Article
- Optimizing Sensor and Data Selection on Lower Limbs via Deep Learning for Real-Time Human Activity Recognition.IEEE transactions on human-machine systems · 2026Article
- Rectus Femoris and Gastrocnemius EMG Driven Cheonjiin Speller for Korean Text Input.Sensors (Basel, Switzerland) · 2025Article
- Are Reusable Dry Electrodes an Alternative to Gelled Electrodes for Canine Surface Electromyography?Animals : an open access journal from MDPI · 2025Article
- An enhanced deep learning framework for muscle artifact removal from ECG signal integrating resnet, GCAB, and BI-LSTM.Physical and engineering sciences in medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.