Optimization of neural network for efficient EMG signal classification

This paper illustrates the classification of Electromyography (EMG) signals through designing and optimization of artificial neural network. The EMG signals obtained for different kinds of hand movements, which are processed to extract the features. Extracted time and time frequency based feature sets are used to train the neural network. A back-propagation neural network with Levenberg-Marquardt training algorithm has been utilized for the classification. The results show that the designed network is optimized for 10 hidden neurons and able to efficiently classify single channel EMG signals with an average rate of 88.4%.