caa a22 4500 606160140 CHVBK 20210128100628.0 cr unu---uuuuu 210128e20150701xx s 000 0 eng 10.1007/s00521-014-1802-y doi (NATIONALLICENCE)springer-10.1007/s00521-014-1802-y EEG data classification using wavelet features selected by Wilcoxon statistics [Elektronische Daten] [Thanh Nguyen, Abbas Khosravi, Douglas Creighton, Saeid 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. The Natural Computing Applications Forum, 2014 Wavelet transformation nationallicence Wilcoxon test nationallicence EEG signal classification nationallicence BCI competition II nationallicence Naïve Bayes classifier nationallicence Nguyen Thanh Centre for Intelligent Systems Research (CISR), Deakin University, Waurn Ponds, VIC, Australia aut Khosravi Abbas Centre for Intelligent Systems Research (CISR), Deakin University, Waurn Ponds, VIC, Australia aut Creighton Douglas Centre for Intelligent Systems Research (CISR), Deakin University, Waurn Ponds, VIC, Australia aut Nahavandi Saeid Centre for Intelligent Systems Research (CISR), Deakin University, Waurn Ponds, VIC, Australia aut Neural Computing and Applications Springer London 26/5(2015-07-01), 1193-1202 0941-0643 26:5<1193 2015 26 521 https://doi.org/10.1007/s00521-014-1802-y text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-014-1802-y text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Nguyen Thanh Centre for Intelligent Systems Research (CISR), Deakin University, Waurn Ponds, VIC, Australia aut NATIONALLICENCE 700 1- Khosravi Abbas Centre for Intelligent Systems Research (CISR), Deakin University, Waurn Ponds, VIC, Australia aut NATIONALLICENCE 700 1- Creighton Douglas Centre for Intelligent Systems Research (CISR), Deakin University, Waurn Ponds, VIC, Australia aut NATIONALLICENCE 700 1- Nahavandi Saeid Centre for Intelligent Systems Research (CISR), Deakin University, Waurn Ponds, VIC, Australia aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/5(2015-07-01), 1193-1202 0941-0643 26:5<1193 2015 26 521