Fuzzy system with tabu search learning for classification of motor imagery data

Nguyen, Thanh, Khosravi, Abbas, Creighton, Douglas and Nahavandi, Saeid 2015, Fuzzy system with tabu search learning for classification of motor imagery data, Biomedical signal processing and control, vol. 20, pp. 61-70, doi: 10.1016/j.bspc.2015.04.007.

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Title Fuzzy system with tabu search learning for classification of motor imagery data
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 Biomedical signal processing and control
Volume number 20
Start page 61
End page 70
Total pages 10
Publisher Elsevier
Publication date 2015-07-01
ISSN 1746-8094
1746-8108
Keyword(s) BCI competition II
EEG signal classification
Motor imagery data
Wavelet transform
Wilcoxon test
Science & Technology
Technology
Life Sciences & Biomedicine
Engineering, Biomedical
Medical Laboratory Technology
Engineering
EPILEPTIC SEIZURE DETECTION
BCI COMPETITION 2003
FEATURE-EXTRACTION
FUNCTION APPROXIMATION
SIGNAL CLASSIFICATION
INFERENCE SYSTEM
EEG SIGNAL
POTENTIALS
MACHINE
Summary This paper introduces an approach to classify EEG signals using wavelet transform and a fuzzy standard additive model (FSAM) with tabu search learning mechanism. Wavelet coefficients are ranked based on statistics of the Wilcoxon test. The most informative coefficients are assembled to form a feature set that serves as inputs to the tabu-FSAM. 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 tabu-FSAM method considerably dominates the competitive classifiers, and outperforms the best performance on the Ia and Ib datasets reported in the BCI competition II.
Language eng
DOI 10.1016/j.bspc.2015.04.007
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30075807

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