Data selection in EEG signals classification

Wang, Shuaifang and Li, Yan and Wen, Peng and Lai, David (2016) Data selection in EEG signals classification. Australasian Physical and Engineering Sciences in Medicine, 39 (1). pp. 157-165. ISSN 0158-9938

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Abstract

The alcoholism can be detected by analyzing electroencephalogram (EEG) signals. However, analyzing multi-channel EEG signals is a challenging task, which often requires complicated calculations and long execution time. This paper proposes three data selection methods to extract representative data from the EEG signals of alcoholics. The methods are the principal component analysis based on graph entropy (PCA-GE), the channel selection based on graph entropy (GE) difference, and the mathematic combinations channel selection, respectively. For comparison purposes, the selected data from the three methods are then classified by three classifiers: the J48 decision tree, the K-nearest neighbor and the Kstar, separately. The experimental results show that the proposed methods are successful in selecting data without compromising the classification accuracy in discriminating the EEG signals from alcoholics and non-alcoholics. Among them, the proposed PCA-GE method uses only 29.69 % of the whole data and 29.5 % of the computation time but achieves a 94.5 % classification accuracy. The channel selection method based on the GE difference also gains a 91.67 % classification accuracy by using only 29.69 % of the full size of the original data. Using as little data as possible without sacrificing the final classification accuracy is useful for online EEG analysis and classification application design.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Accepted version deposited in accordance with the copyright policy of the publisher.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 05 Jul 2016 01:31
Last Modified: 09 Jan 2017 23:05
Uncontrolled Keywords: EEG; data selection; horizontal visibility graph (HVG); principal component analysis (PCA)
Fields of Research : 01 Mathematical Sciences > 0104 Statistics > 010401 Applied Statistics
08 Information and Computing Sciences > 0806 Information Systems > 080607 Information Engineering and Theory
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080106 Image Processing
Socio-Economic Objective: C Society > 92 Health > 9204 Public Health (excl. Specific Population Health) > 920410 Mental Health
Identification Number or DOI: 10.1007/s13246-015-0414-x
URI: http://eprints.usq.edu.au/id/eprint/28810

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