A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Wavelets and filter banks: theory and design
IEEE Transactions on Signal Processing
Entropy-based algorithms for best basis selection
IEEE Transactions on Information Theory - Part 2
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
An adaptive feature extractor for gesture SEMG recognition
ICMB'08 Proceedings of the 1st international conference on Medical biometrics
Computers in Biology and Medicine
Classification of EMG signals using combined features and soft computing techniques
Applied Soft Computing
Muscle activity detection in electromyograms recorded during periodic movements
Computers in Biology and Medicine
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Features can be classified into interferential features and discriminable features according to their contribution to pattern recognition. In this paper, a novel and simple method based on wavelet packet transform is proposed to extract the features from surface EMG signal. In this method, the features are relative wavelet packet energy (RWPE), which is evaluated from several selected frequency bands of surface EMG signal. Compared with a conventional method, which is of the best performance in previous applications, the method can compress the interferential features and enhance the discriminable features more effectively. In consequence, the RWPE features calculated by the method represent different patterns of surface EMG signal more accurately and the accuracy of surface EMG signal pattern classification is improved greatly.