The application of mutual information-based feature selection and fuzzy LS-SVM-based classifier in motion classification.

This paper presents an effective mutual information-based feature selection approach for EMG-based motion classification task. The wavelet packet transform (WPT) is exploited to decompose the four-class motion EMG signals to the successive and non-overlapped sub-bands. The energy characteristic of each sub-band is adopted to construct the initial full feature set. For reducing the computation complexity, mutual information (MI) theory is utilized to get the reduction feature set without compromising classification accuracy. Compared with the extensively used feature reduction methods such as principal component analysis (PCA), sequential forward selection (SFS) and backward elimination (BE) etc., the comparison experiments demonstrate its superiority in terms of time-consuming and classification accuracy. The proposed strategy of feature extraction and reduction is a kind of filter-based algorithms which is independent of the classifier design. Considering the classification performance will vary with the different classifiers, we make the comparison between the fuzzy least squares support vector machines (LS-SVMs) and the conventional widely used neural network classifier. In the further study, our experiments prove that the combination of MI-based feature selection and SVM techniques outperforms other commonly used combination, for example, the PCA and NN. The experiment results show that the diverse motions can be identified with high accuracy by the combination of MI-based feature selection and SVM techniques. Compared with the combination of PCA-based feature selection and the classical Neural Network classifier, superior performance of the proposed classification scheme illustrates the potential of the SVM techniques combined with WPT and MI in EMG motion classification.