Using LBG algorithm for extracting the features of EMG signals

The Electromyographic (EMG) signals observed at the surface of the skin is the sum of many small action potentials generated in the muscle fibers. There is only a pattern for each EMG signals, which are generated by biceps and triceps muscles. There are different types of signal processing in order to find out the feature values for true classification in this pattern. In this study, the Feature values belong to 4 different arm movements are obtained by using clustering methods, i.e K-means, Fuzzy C-means, and LBG after applying Wavelet Transform to EMG signals . Then these feature values are compared each other by KEYK and Quadratic Discriminant Analysis classifier.