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EMG based input device is a natural means of human computer interface (HCI) because the electrical activity induced by the human's arm muscle movements can be interpreted and transformed into computer's control commands. In this paper, we describe an approach for classifying electromyography (EMG) signals using a multilayer perceptron neural network (MLP) and Bayesian classifier (BC) with the wavelet transformation technique for feature selection to discriminate 6 classes of motions to control a mouse. Wavelet Transformation (WT) was applied to raw EMG data and in order to decrease the dimension of the feature sets, principle component analysis (PCA) and sequential forward selection (SFS) were utilized.
Date of Conference: 14-17 Oct. 2012