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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.

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Title EEG data classification using wavelet features selected by Wilcoxon statistics
Author(s) Nguyen, ThanhORCID iD for Nguyen, Thanh orcid.org/0000-0001-9709-1663
Khosravi, AbbasORCID iD for Khosravi, Abbas orcid.org/0000-0001-6927-0744
Creighton, DouglasORCID iD for Creighton, Douglas orcid.org/0000-0002-9217-1231
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
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
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Created: Tue, 10 Mar 2015, 11:47:18 EST

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