Classification of EMG Signals by Spectral Methods and SVM Classifier

Abstract

In this study, EMG signals taken from the skin surface as a result of muscles' contraction are classified. Studied EMG signals include 400 different patterns relating to four different movements. Each pattern is obtained by adding EMG signals one after another, which are recorded synchronously from two different muscles relating to one movement. Support Vector Machine (SVM) classifier, a supervised method, is used to classify these pattterns. But signals need to be preprocessed before being used in SVM classifier. To this end, spectral methods are consulted. In this way, feature vectors which are more significant than raw data and are composed of coefficients are achieved. Four different methods are used for preprocessing and feature vectors obtained are classified by SVM. Success of SVM classifier is tested and performances of preprocessing methods are compared. Best achievement is 94.25%. Keywords: EMG; Spectral Methods; Autoregressive (AR); SVM Classifier.