title: Wavelet Packet Feature Assessment for High-Density Myoelectric Pattern Recognition and Channel Selection toward Stroke Rehabilitation.
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.
authors.affiliation: Department of Electronic Science and Technology, University of Science and Technology of China , Hefei , China.
authors.forename: Dongqing
authors.initials: D
authors.lastname: Wang
authors.affiliation: Department of Electronic Science and Technology, University of Science and Technology of China , Hefei , China.
authors.forename: Xu
authors.initials: X
authors.lastname: Zhang
authors.affiliation: Department of Rehabilitation Medicine, First Affiliated Hospital of Anhui Medical University , Hefei , China.
authors.forename: Xiaoping
authors.initials: X
authors.lastname: Gao
authors.affiliation: Department of Electronic Science and Technology, University of Science and Technology of China , Hefei , China.
authors.forename: Xiang
authors.initials: X
authors.lastname: Chen
authors.affiliation: Department of Physical Medicine and Rehabilitation, University of Texas Health Science Center at Houston, Houston, TX, USA; TIRR Memorial Hermann Research Center, Houston, TX, USA; Guangdong Work Injury Rehabilitation Center, Guangzhou, China.
authors.forename: Ping
authors.initials: P
authors.lastname: Zhou
keywords: channel selection; myoelectric control; pattern recognition; stroke rehabilitation; wavelet packet transform