Classification Temporal Attribute of EMG Signals

Abstract

There are a considerable number of feature extraction methods to be used for the classification of electromyographic (EMG) signals. These features are obtained from the raw EMG data by time domain and time-frequency domain transformations. Time-frequency domain originated features involve a high computational cost. Hence, for the EMG controlled electromechanical prostheses to be readily usable, time domain features are utilized. Previous studies revealed that for a better utilization of the EMG controlled prostheses, the complete signal processing period should be less than 300 ms. In this study, the classification performances of the features in the time domain will be compared. ANFIS neural network has been preferred as the classification structure in line with the wide experience in the literature. The purpose of this study is to put forward a conceptual viewpoint related to the choice of features to be classified in the work towards EMG controlled prostheses development. © 2012 IEEE.