Techniques for Feature Extraction from EMG Signal

The myoelectric signal (MES) is one of the biosignals utilized in helping humans to control equipments. For this we required to recognize the hand movement. In this direction the first step is feature extraction. The optimal feature is important for the achievement in EMG analysis and control. By this extracted feature we reduce the computational cost of a multifunction myoelectric control system. The goal of this paper is to define the methods and approaches which are most suited for extracting the features from EMG signal. The techniques discussed here are spectral approaches like STFT, Thompson transform etc, wavelet based analysis, fuzzy based feature extracter and temporal approaches.