AUTHOR=Wang Dongqing , Zhang Xu , Gao Xiaoping , Chen Xiang , Zhou Ping TITLE=Wavelet Packet Feature Assessment for High-Density Myoelectric Pattern Recognition and Channel Selection toward Stroke Rehabilitation JOURNAL=Frontiers in Neurology VOLUME=7 YEAR=2016 URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2016.00197 DOI=10.3389/fneur.2016.00197 ISSN=1664-2295 ABSTRACT=

This study presents wavelet packet feature assessment of neural control information in paretic upper limb muscles of stroke survivors for myoelectric pattern recognition, taking advantage of high-resolution time–frequency representations of surface electromyogram (EMG) signals. On this basis, a novel channel selection method was developed by combining the Fisher’s class separability index and the sequential feedforward selection analyses, in order to determine a small number of appropriate EMG channels from original high-density EMG electrode array. The advantages of the wavelet packet features and the channel selection analyses were further illustrated by comparing with previous conventional approaches, in terms of classification performance when identifying 20 functional arm/hand movements implemented by 12 stroke survivors. This study offers a practical approach including paretic EMG feature extraction and channel selection that enables active myoelectric control of multiple degrees of freedom with paretic muscles. All these efforts will facilitate upper limb dexterity restoration and improved stroke rehabilitation.