The Study of EMG Channel Reduction for Hand Grasping Classification

Abstract

Using electromyogram (EMG) signals as a human-machine interface to control the myoelectric prostheses is consummately advanced. Nevertheless, reported studies present high classification performance from the EMG signal captured in laboratory conditions. There is a very challenging suffix regarding untrained condition. Limb position change causes high variation in EMG signals resulting in high misclassification rate in the untrained position. The goal of this work is to indicate the most relevance EMG channel, which is robust to the effect of a limb position change. The EMG signals from four different object shapes with five different object placement positions were examined using linear discriminant analysis. To determine the best EMG channel subset of all possible combination of selective channels, we reduced the number of EMG channels from 12 to 3. The results have shown that the minimum classification error rate (2.09%) across five subjects can be achieved by use of eight optimal EMG channels. Additionally, our results suggest the EMG channel number 3 (Triceps brachii), 7 (Extensor digitorum communis), and 9 (Brachioradialis) as the most three emphasized channels that contain the rich information of hand grasping and generalize limb position change.