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EEG data classification using wavelet features selected by Wilcoxon statistics

Version 2 2024-06-05, 11:47
Version 1 2015-03-10, 12:47
journal contribution
posted on 2024-06-05, 11:47 authored by T Nguyen, Abbas KhosraviAbbas Khosravi, Douglas CreightonDouglas Creighton, S Nahavandi
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.

History

Journal

Neural computing and applications

Volume

26

Pagination

1193-1202

Location

Berlin, Germany

ISSN

0941-0643

Language

eng

Publication classification

C Journal article, C1 Refereed article in a scholarly journal

Copyright notice

2014, Springer

Issue

5

Publisher

Springer