EEG data classification using wavelet features selected by Wilcoxon statistics Nguyen, Thanh, Khosravi, Abbas, Creighton, Douglas and Nahavandi, Saeid 2015, EEG data classification using wavelet features selected by Wilcoxon statistics, Neural computing and applications, vol. 26, no. 5, pp. 1193-1202, doi: 10.1007/s00521-014-1802-y. Attached Files Name Description MIMEType Size Downloads Title EEG data classification using wavelet features selected by Wilcoxon statistics Author(s) Nguyen, Thanh orcid.org/0000-0001-9709-1663 Khosravi, Abbas orcid.org/0000-0001-6927-0744 Creighton, Douglas Nahavandi, Saeid Journal name Neural computing and applications Volume number 26 Issue number 5 Start page 1193 End page 1202 Total pages 10 Publisher Springer Place of publication Berlin, Germany Publication date 2015-07 ISSN 0941-0643 Keyword(s) BCI competition II EEG signal classification Naïve Bayes classifier Wavelet transformation Wilcoxon test Summary This paper introduces a method to classify EEG signals using features extracted by an integration of wavelet transform and the nonparametric Wilcoxon test. Orthogonal Haar wavelet coefficients are ranked based on the Wilcoxon test’s statistics. The most prominent discriminant wavelets are assembled to form a feature set that serves as inputs to the naïve Bayes classifier. Two benchmark datasets, named Ia and Ib, downloaded from the brain–computer interface (BCI) competition II are employed for the experiments. Classification performance is evaluated using accuracy, mutual information, Gini coefficient and F-measure. Widely used classifiers, including feedforward neural network, support vector machine, k-nearest neighbours, ensemble learning Adaboost and adaptive neuro-fuzzy inference system, are also implemented for comparisons. The proposed combination of Haar wavelet features and naïve Bayes classifier considerably dominates the competitive classification approaches and outperforms the best performance on the Ia and Ib datasets reported in the BCI competition II. Application of naïve Bayes also provides a low computational cost approach that promotes the implementation of a potential real-time BCI system. Language eng DOI 10.1007/s00521-014-1802-y Field of Research 080108 Neural, Evolutionary and Fuzzy Computation Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category C1 Refereed article in a scholarly journal ERA Research output type C Journal article HERDC collection year 2014 Copyright notice ©2014, Springer Persistent URL http://hdl.handle.net/10536/DRO/DU:30070446 Document type: Journal Article Collection: Centre for Intelligent Systems Research Related Links Link Description Connect to published version Go to link with your DU access privileges   Connect to Elements publication management system Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Tue, 10 Mar 2015, 11:47:18 EST Wed, 11 Mar 2015, 04:05:13 EST Wed, 11 Mar 2015, 07:15:35 EST Thu, 26 Mar 2015, 08:51:59 EST Thu, 26 Mar 2015, 08:52:42 EST Wed, 22 Apr 2015, 12:05:15 EST Thu, 23 Apr 2015, 14:20:00 EST Thu, 23 Apr 2015, 14:21:17 EST Wed, 13 May 2015, 15:43:16 EST Thu, 28 May 2015, 15:20:19 EST Fri, 07 Aug 2015, 10:56:54 EST Fri, 07 Aug 2015, 11:04:55 EST Wed, 14 Oct 2015, 14:16:51 EST Fri, 21 Apr 2017, 07:05:45 EST Filtered Full Citation counts:   Cited 3 times in TR Web of Science Cited 1 times in Scopus Search Google Scholar Access Statistics: 237 Abstract Views, 3 File Downloads  -  Detailed Statistics Created: Tue, 10 Mar 2015, 11:47:18 EST

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