Analysis and classification of EEG signals using a hybrid clustering technique

Siuly, and Li, Y. and Wen, P. (2010) Analysis and classification of EEG signals using a hybrid clustering technique. In: 2010 IEEE/ICME International Conference on Complex Medical Engineering (CME 2010), 13-15 July 2010, Gold Coast, Australia.

Abstract

This paper presents a novel hybrid approach based on clustering technique (CT) and least square support vector machine (LS-SVM) denoted as CT-LS-SVM for classifying two-class EEG signals. The study aims to extract representative features from the original EEG data through the CT method and then to classify two-class EEG signals by the LS-SVM using these features as inputs. In order to test the effectiveness of the proposed method, the experiment is carried out on an epileptic EEG data and a mental imagery tasks EEG data. The classification accuracy of the current method is compared to the previous reported methods of the literature. The proposed approach is found to achieve an average classification accuracy of 99.19% for the mental imagery tasks EEG data and 94.18% for the epileptic EEG data. Our results show the highest classification accuracy (99.90%) for healthy subjects with eyes open (Set A) and epileptic patients during seizure activity (Set E) from the epileptic EEG data among the reported algorithms. Thus, the findings of the current research demonstrate that the CT method is efficient for extracting features representing the EEG signals and the LS-SVM classifier has the inherent ability to solve a pattern recognition task for these features.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: © 2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Depositing User: Mrs . Siuly
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 04 Feb 2011 02:08
Last Modified: 09 Oct 2014 05:12
Uncontrolled Keywords: CT method; EEG signals; clustering technique; hybrid clustering technique; image classification; least square support vector machine; pattern recognition
Fields of Research (FOR2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
10 Technology > 1004 Medical Biotechnology > 100402 Medical Biotechnology Diagnostics (incl. Biosensors)
11 Medical and Health Sciences > 1109 Neurosciences > 110999 Neurosciences not elsewhere classified
Socio-Economic Objective (SEO2008): E Expanding Knowledge > 97 Expanding Knowledge > 970109 Expanding Knowledge in Engineering
Identification Number or DOI: doi: 10.1109/ICCME.2010.5558875
URI: http://eprints.usq.edu.au/id/eprint/9245

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