Next Article in Journal
NO and NO2 Sensing Properties of WO3 and Co3O4 Based Gas Sensors
Next Article in Special Issue
Model-Based Spike Detection of Epileptic EEG Data
Previous Article in Journal
Teaching Human Poses Interactively to a Social Robot
Previous Article in Special Issue
An Acetylcholinesterase-Based Chronoamperometric Biosensor for Fast and Reliable Assay of Nerve Agents
Article Menu

Export Article

Open AccessReview
Sensors 2013, 13(9), 12431-12466; doi:10.3390/s130912431

Surface Electromyography Signal Processing and Classification Techniques

Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi, Selangor 43600, Malaysia
School of Electrical and Electronics Engineering, Chung Ang University, 221 Hueksuk-Dong, Dongjak-Gu, Seoul 156-756, Korea
Author to whom correspondence should be addressed.
Received: 20 July 2013 / Revised: 21 August 2013 / Accepted: 11 September 2013 / Published: 17 September 2013
(This article belongs to the Special Issue Biomedical Sensors and Systems)
View Full-Text   |   Download PDF [1288 KB, 21 June 2014; original version 21 June 2014]   |  


Electromyography (EMG) signals are becoming increasingly important in many applications, including clinical/biomedical, prosthesis or rehabilitation devices, human machine interactions, and more. However, noisy EMG signals are the major hurdles to be overcome in order to achieve improved performance in the above applications. Detection, processing and classification analysis in electromyography (EMG) is very desirable because it allows a more standardized and precise evaluation of the neurophysiological, rehabitational and assistive technological findings. This paper reviews two prominent areas; first: the pre-processing method for eliminating possible artifacts via appropriate preparation at the time of recording EMG signals, and second: a brief explanation of the different methods for processing and classifying EMG signals. This study then compares the numerous methods of analyzing EMG signals, in terms of their performance. The crux of this paper is to review the most recent developments and research studies related to the issues mentioned above.
Keywords: electromyography; noise source; wavelet; EMD; ICA; artificial neural network; HOS electromyography; noise source; wavelet; EMD; ICA; artificial neural network; HOS
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Chowdhury, R.H.; Reaz, M.B.I.; Ali, M.A.B.M.; Bakar, A.A.A.; Chellappan, K.; Chang, T.G. Surface Electromyography Signal Processing and Classification Techniques. Sensors 2013, 13, 12431-12466.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top