EMG signal classification using conic section function neural networks

The aim of this work is to classify EMG signals using a new neural network architecture to control multifunction prostheses. The control of these prostheses can be made using myoelectric signals taken from a single pair of surface electrodes. This case has been demonstrated specifically for use by above elbow amputees. The ability to separate different muscle contraction characters depends on myoelectric signal information. Therefore, the classification of these signals is investigated. The proposed neural network algorithm here makes the user learn better and faster.