Classification of EEG signals using sampling techniques and least square support vector machines

Siuly, and Li, Yan and Wen, Peng (2009) Classification of EEG signals using sampling techniques and least square support vector machines. In: RSKT 2009: 4th International Conference on Rough Sets and Knowledge Technology , 14-16 Jul 2009, Gold Coast, Australia.

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Abstract

This paper presents sampling techniques (ST) concept for feature extraction from electroencephalogram (EEG) signals. It describes the application of least square support vector machine (LS-SVM) that executes the classification of EEG signals from two classes, namely normal persons with eye open and epileptic patients during epileptic seizure activity. Decision-making has been carried out in two stages. In the first stage, ST has been used to extract the representative features of EEG time series data and to reduce the dimensionality of that data, and in the second stage, LS-SVM has been applied on the extracted feature vectors to classify EEG signals between normal persons and epileptic patients. In this study, the performance of the LS-SVM is demonstrated in terms of training and testing performance separately and then a comparison is made between them. The experimental results show that the classification accuracy for the training and testing data are 80.31% and 80.05% respectively. This research demonstrates that ST is well suited for feature extraction since selected samples maintain the most important images of the original data and LS-SVM has great potential in classifying the EEG signals.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Series: Lecture Notes in Computer Science, v. 5589. Print copy held USQ Library 006.3 Rou. This authors' version of the work is posted here with permission of the publisher for your personal use. No further distribution is permitted.
Depositing User: Dr Yan Li
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 24 Jan 2010 02:21
Last Modified: 02 Jul 2013 23:37
Uncontrolled Keywords: sampling techniques (ST); simple random sampling (SRS); least square support vector machines (LS-SVM); electroencephalogram (EEG)
Fields of Research (FOR2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
09 Engineering > 0906 Electrical and Electronic Engineering > 090609 Signal Processing
11 Medical and Health Sciences > 1109 Neurosciences > 110999 Neurosciences not elsewhere classified
Socio-Economic Objective (SEO2008): E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: doi: 10.1007/978-3-642-02962-2_47
URI: http://eprints.usq.edu.au/id/eprint/6649

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