Techniques of EMG signal analysis: detection, processing, classification and applications

By Mamun Bin Ibne Reaz, M. Hussain, F. Mohd-Yasin. Biological Procedures Online, 2006

1,438 citations recorded by the source corpus.

Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development, and modern human computer interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. The purpose of this paper is to illustrate the various methodologies and algorithms for EMG signal analysis to provide efficient and effective ways of understanding the signal and its nature. We further point up some of the hardware implementations using EMG focusing on applications related to prosthetic hand control, grasp recognition, and human computer interaction. A comparison study is also given to show performance of various EMG signal analysis methods. This paper provides researchers a good understanding of EMG signal and its analysis procedures. This knowledge will help them develop more powerful, flexible, and efficient applications.

Subfields: Biomedical Engineering, Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/d867bc7dca7002e400218288541e6e5a6e808220

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In Biomedical Engineering

In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

A Review of Classification Techniques of EMG Signals during Isotonic and Isometric Contractions (2016, background): “, the combination of muscle fiber action potentials from all the muscle fibers of a single motor unit is called as motor unit action potential (MUAP) [8].”
A Novel Feature Extraction for Robust EMG Pattern Recognition (2009, methodology): “…three types of noise can be removed using typical filtering procedures such as band-pass filter, band-stop filter, or the use of well electrode and instrument [3-4] but the interferences of random noise that fall in EMG dominant frequency energy is difficult to be removed using previous procedures.”
Surface Electromyography Signal Processing and Classification Techniques (2013, background): “Within the past few decades, there has been considerable interest in using higher order statistics (HOS) technique [15].”
A Review of Non-Invasive Techniques to Detect and Predict Localised Muscle Fatigue (2011, methodology): “One common method for sEMG classification is to measure the Euclidean distance between the MUAPs waveform, where a shimmer is generated in the representation of time-triggered and non-overlapping MUAPs [36].”
Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning (2018, methodology): “Both the Morlet and Mexican Hat wavelet were considered for this work due to their previous application in EMG-related work [53], [54].”
Demonstrating the feasibility of using forearm electromyography for muscle-computer interfaces (2008, methodology): “These classes were chosen based on prior work suggesting that they may be discriminative of various activities within electroencephalography and EMG signals [14,17,22].”
Emotional facial expressions evoke faster orienting responses, but weaker emotional responses at neural and behavioural levels compared to scenes: A simultaneous EEG and facial EMG study (2016, positive): “We used dual channel electrode cables with carbon coating and active shielding technology for low noise and an additional ground electrode cable attached to the back of the neck (see Reaz et al., 2006; Wand, 2015).”