EEG signal analysis for BCI application using fuzzy system

Nguyen, Thanh, Nahavandi, Saeid, Khosravi, Abbas, Creighton, Douglas and Hettiarachchi, Imali 2015, EEG signal analysis for BCI application using fuzzy system, in IJCNN 2015: Proceedings of the 2015 International Joint Conference on Neural Networks, IEEE, Piscataway, N.J., pp. 1-8, doi: 10.1109/IJCNN.2015.7280593.

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Title EEG signal analysis for BCI application using fuzzy system
Author(s) Nguyen, ThanhORCID iD for Nguyen, Thanh orcid.org/0000-0001-9709-1663
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Khosravi, AbbasORCID iD for Khosravi, Abbas orcid.org/0000-0001-6927-0744
Creighton, DouglasORCID iD for Creighton, Douglas orcid.org/0000-0002-9217-1231
Hettiarachchi, ImaliORCID iD for Hettiarachchi, Imali orcid.org/0000-0002-4220-0970
Conference name International Joint Conference on Neural Networks (2015: Killarney, Ireland)
Conference location Killarney, Ireland
Conference dates 12-17 Jul. 2015
Title of proceedings IJCNN 2015: Proceedings of the 2015 International Joint Conference on Neural Networks
Publication date 2015
Start page 1
End page 8
Total pages 8
Publisher IEEE
Place of publication Piscataway, N.J.
Summary An approach to EEG signal classification for brain-computer interface (BCI) application using fuzzy standard additive model is introduced in this paper. The Wilcoxon test is employed to rank wavelet coefficients. Top ranking wavelets are used to form a feature set that serves as inputs to the fuzzy classifiers. Experiments are carried out using two benchmark datasets, Ia and Ib, downloaded from the BCI competition II. Prevalent 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. Experimental results show the dominance of the proposed method against competing approaches.
ISBN 9781479919604
Language eng
DOI 10.1109/IJCNN.2015.7280593
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30082492

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