Title: EEG signal classification based on simple random sampling technique with least square support vector machine

Authors: Siuly; Yan Li; Peng Wen

Addresses: Department of Mathematics and Computing, Centre for Systems Biology, University of Southern Queensland, Toowoomba, QLD 4350, Australia. ' Department of Mathematics and Computing, Centre for Systems Biology, University of Southern Queensland, Toowoomba, QLD 4350, Australia. ' Faculty of Engineering and Surveying, Centre for Systems Biology, University of Southern Queensland, Toowoomba, QLD 4350, Australia

Abstract: This paper proposes a new approach based on Simple Random Sampling (SRS) technique with Least Square Support Vector Machine (LS-SVM) to classify two-class of electroencephalogram (EEG) signals. The experiments are carried out on two EEG databases and a synthetic Ripley dataset. All two-class pairs are tested and our proposed approach obtains a 95.58% average classification accuracy for the EEG epileptic database, 98.73% for the mental imagery tasks EEG database and 100% for Ripley data. We compare our method with two most recent methods for the epileptic database. Experimental results demonstrate that the proposed method is more promising than previously reported classification techniques.

Keywords: EEG signals; electroencephalograms; random sampling; LS-SVM; least squares SVMs; support vector machines; signal classification; feature extraction; epilepsy.

DOI: 10.1504/IJBET.2011.044417

International Journal of Biomedical Engineering and Technology, 2011 Vol.7 No.4, pp.390 - 409

Published online: 21 Jan 2015 *

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