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

Siuly, and Li, Yan and Wen, Peng (2011) EEG signal classification based on simple random sampling technique with least square support vector machine. International Journal of Biomedical Engineering and Technology, 7 (4). pp. 390-409. ISSN 1752-6418

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.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to published version due to publisher copyright policy.
Depositing User: Mrs . Siuly
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 05 Jan 2012 02:15
Last Modified: 03 Jul 2013 00:56
Uncontrolled Keywords: EEG; electroencephalogram; simple random sampling technique; LS-SVM; least square support vector machine; signal classification; feature extraction.
Fields of Research (FOR2008): 10 Technology > 1004 Medical Biotechnology > 100402 Medical Biotechnology Diagnostics (incl. Biosensors)
09 Engineering > 0906 Electrical and Electronic Engineering > 090609 Signal Processing
09 Engineering > 0903 Biomedical Engineering > 090302 Biomechanical Engineering
Socio-Economic Objective (SEO2008): E Expanding Knowledge > 97 Expanding Knowledge > 970110 Expanding Knowledge in Technology
Identification Number or DOI: doi: 10.1504/IJBET.2011.044417
URI: http://eprints.usq.edu.au/id/eprint/20310

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