Abstract: 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
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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.
Chowdhury RH, Reaz MBI, Ali MABM, Bakar AAA, Chellappan K, Chang TG. Surface Electromyography Signal Processing and Classification Techniques. Sensors. 2013; 13(9):12431-12466.
Chowdhury, Rubana H.; Reaz, Mamun B.I.; Ali, Mohd A.B.M.; Bakar, Ashrif A.A.; Chellappan, Kalaivani; Chang, Tae G. 2013. "Surface Electromyography Signal Processing and Classification Techniques." Sensors 13, no. 9: 12431-12466.