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Title: Feature-channel subset selection for optimising myoelectric human-machine interface design

Authors: Mohammadreza Asghari Oskoei; Huosheng Hu; John Q. Gan

Addresses: Faculty of Mathematics and Computer Science, University of Allameh Tabataba'i, Tehran 15136-15411, Iran; School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK ' School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK ' School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK

Abstract: This paper proposes a feature-channel subset selection method for obtaining an optimal subset of features and channels of myoelectric human-machine interface applied to classify upper limb's motions using multi-channel myoelectric signals. It employs a multi-objective genetic algorithm as a search strategy and either data separability index or classification rate as an objective function. A wide range of features in time, frequency, and time-scale domains are examined, and a modification that reduces the bias of cardinality in the separability index is evaluated. The proposed method produces a compact subset of features and channels, which results in high accuracy by linear classifiers without a need of preliminary tailor-made adjustments.

Keywords: myoelectric HMI; human-machine interface; interface design; feature subset selection; multi-objective genetic algorithms; MOGA; Davies-Bouldin index; DBI; upper limb motions; linear classifiers.

DOI: 10.1504/IJBBR.2013.058708

International Journal of Biomechatronics and Biomedical Robotics, 2013 Vol.2 No.2/3/4, pp.195 - 208

Available online: 17 Jan 2014 *

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