Paper Title
Compare between EMG Signal Classification for Hand Movements using ANN and ANN-PSO

Abstract
The classification of EMG signal for different hand movement using artificial neural network have been proposed in this paper. In this study, the EMG is used for measuring the electrical activities of muscle cell. the muscles have different signals, so each one has their own signals, which is changed with every movement. In this study, the main aim is to distinguish between different hand movements in the same position (close hand, resting hand and open hand). Artificial neural network is used for classifying hand movements in MATLAB using different training methods (cgf, gda, gdx, rp, lm and scg) then comparing the accuracy with the other results that obtained from ANN-PSO where PSO method used for training the neural network. The accuracy of classification was varied between (88%-95%) using Artificial Neural Network (ANN) with the seven training methods while the accuracy reached 96% when using ANN-PSO for 9 hidden neurons layer. Keywords - Neural Network, Particle Swarm Optimization, EMG Signals, Hand Movement, Medical Electronics