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EEG signal classification for BCI applications by wavelets and interval type-2 fuzzy logic systems
journal contribution
posted on 2015-06-01, 00:00 authored by Thanh Thi NguyenThanh Thi Nguyen, Abbas KhosraviAbbas Khosravi, Douglas CreightonDouglas Creighton, Saeid NahavandiThe 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.
History
Journal
Expert systems with applicationsVolume
42Issue
9Pagination
4370 - 4380Publisher
ElsevierLocation
Amsterdam, The NetherlandsPublisher DOI
ISSN
0957-4174Language
engPublication classification
C Journal article; C1 Refereed article in a scholarly journalCopyright notice
2015, ElsevierUsage metrics
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No categories selectedKeywords
BCI competition IIEEG signal classificationInterval type-2 fuzzy logic systemReceiver operating characteristics (ROC) curveWavelet transformationScience & TechnologyTechnologyComputer Science, Artificial IntelligenceEngineering, Electrical & ElectronicOperations Research & Management ScienceComputer ScienceEngineeringReceiver operating characteristics (ROC)curveEPILEPTIC SEIZURE DETECTIONFEATURE-EXTRACTIONINFERENCE SYSTEMCOMPETITION 2003POTENTIALSTRANSFORM
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