A novel statistical algorithm for multiclass EEG signal classification

Siuly, and Li, Yan (2014) A novel statistical algorithm for multiclass EEG signal classification. Engineering Applications of Artificial Intelligence, 34. pp. 154-167. ISSN 0952-1976


This paper presents a new algorithm for the classification of multiclass EEG signals. This algorithm involves applying the optimum allocation technique to select representative samples that reflect an entire database. This research investigates whether the optimum allocation is suitable to extract representative samples depending on their variability within the groups in the input EEG data. It also assesses whether these samples are efficient for the multiclass least square support vector machine (MLS-SVM) to classify EEG signals. The performances of the MLS-SVM with four different output coding approaches: minimum output codes (MOC), error correcting output codes (ECOC), One vs One (1vs1) and One vs All (1vsA), are evaluated with a benchmark epileptic EEG database. To test the consistency, all experiments are repeated ten times with the same classifying parameters in each classification process. The results show very high classification performances for each class, and also confirm the consistency of the proposed method in each repeated experiment. In addition, the performances by the optimum allocation based MLS-SVM method are compared with the four existing reference methods using the same database. The outcomes of this research demonstrate that the optimum allocation is very effective and efficient for extracting the representative patterns from the multiclass EEG data, and the MLS-SVM is also very well fitted with the optimum allocation technique for the EEG classification.

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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: © 2014 Elsevier Ltd. Permanent restricted access to published version due to publisher copyright policy.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Mechanical and Electrical Engineering
Date Deposited: 14 Jan 2015 03:55
Last Modified: 07 Jul 2016 06:01
Uncontrolled Keywords: electroencephalogram (EEG); multiclass classification; multiclass least square support vector machine (MLS-SVM); optimum allocation
Fields of Research : 09 Engineering > 0906 Electrical and Electronic Engineering > 090609 Signal Processing
11 Medical and Health Sciences > 1103 Clinical Sciences > 110320 Radiology and Organ Imaging
11 Medical and Health Sciences > 1109 Neurosciences > 110999 Neurosciences not elsewhere classified
Socio-Economic Objective: C Society > 92 Health > 9202 Health and Support Services > 920203 Diagnostic Methods
Identification Number or DOI: 10.1016/j.engappai.2014.05.011
URI: http://eprints.usq.edu.au/id/eprint/26546

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