Research Repository

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

Oskoei, MA and Hu, H and Gan, JQ (2013) 'Feature-channel subset selection for optimising myoelectric human-machine interface design.' International Journal of Biomechatronics and Biomedical Robotics, 2 (2/3/4). 195 - 208. ISSN 1757-6792

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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.

Item Type: Article
Uncontrolled Keywords: myoelectric HMI; feature subset selection; multi-objective genetic algorithm; Davies-Bouldin index; DBI
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Users 161 not found.
Date Deposited: 09 Sep 2014 10:41
Last Modified: 17 Aug 2017 17:52

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