EEG signal classification for BCI applications by wavelets and interval type-2 fuzzy logic systems Nguyen, Thanh, Khosravi, Abbas, Creighton, Douglas and Nahavandi, Saeid 2015, EEG signal classification for BCI applications by wavelets and interval type-2 fuzzy logic systems, Expert systems with applications, vol. 42, no. 9, pp. 4370-4380. Attached Files Name Description MIMEType Size Downloads Title EEG signal classification for BCI applications by wavelets and interval type-2 fuzzy logic systems Author(s) Nguyen, Thanh orcid.org/0000-0001-9709-1663 Khosravi, Abbas orcid.org/0000-0001-6927-0744 Creighton, Douglas orcid.org/0000-0002-9217-1231 Nahavandi, Saeid orcid.org/0000-0002-0360-5270 Journal name Expert systems with applications Volume number 42 Issue number 9 Start page 4370 End page 4380 Total pages 11 Publisher Elsevier Place of publication Amsterdam, The Netherlands Publication date 2015-06-01 ISSN 0957-4174 Keyword(s) BCI competition II EEG signal classification Interval type-2 fuzzy logic system Receiver operating characteristics (ROC) curve Wavelet transformation Science & Technology Technology Computer Science, Artificial Intelligence Engineering, Electrical & Electronic Operations Research & Management Science Computer Science Engineering Receiver operating characteristics (ROC) curve EPILEPTIC SEIZURE DETECTION FEATURE-EXTRACTION INFERENCE SYSTEM COMPETITION 2003 SETS POTENTIALS TRANSFORM Summary The nonlinear, noisy and outlier characteristics of electroencephalography (EEG) signals inspire the employment of fuzzy logic due to its power to handle uncertainty. This paper introduces an approach to classify motor imagery EEG signals using an interval type-2 fuzzy logic system (IT2FLS) in a combination with wavelet transformation. Wavelet coefficients are ranked based on the statistics of the receiver operating characteristic curve criterion. The most informative coefficients serve as inputs to the IT2FLS for the classification task. 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, sensitivity, specificity and F-measure. Widely-used classifiers, including feedforward neural network, support vector machine, k-nearest neighbours, AdaBoost and adaptive neuro-fuzzy inference system, are also implemented for comparisons. The wavelet-IT2FLS method considerably dominates the comparable classifiers on both datasets, and outperforms the best performance on the Ia and Ib datasets reported in the BCI competition II by 1.40% and 2.27% respectively. The proposed approach yields great accuracy and requires low computational cost, which can be applied to a real-time BCI system for motor imagery data analysis. Language eng 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 Persistent URL http://hdl.handle.net/10536/DRO/DU:30075808 Document type: Journal Article Collections: Institute for Intelligent Systems Research and Innovation (IISRI) GTP Research   Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Thu, 20 Aug 2015, 14:01:23 EST Fri, 21 Aug 2015, 05:06:16 EST Fri, 21 Aug 2015, 08:47:59 EST Sat, 22 Aug 2015, 06:14:21 EST Thu, 03 Sep 2015, 14:40:57 EST Thu, 03 Sep 2015, 14:41:46 EST Filtered Full Citation counts:   Cited 49 times in TR Web of Science Cited 53 times in Scopus Search Google Scholar Access Statistics: 643 Abstract Views, 2 File Downloads  -  Detailed Statistics Created: Fri, 21 Aug 2015, 08:47:59 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.