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EEG signal analysis for BCI application using fuzzy system
conference contribution
posted on 2015-01-01, 00:00 authored by Thanh Thi NguyenThanh Thi Nguyen, Saeid Nahavandi, Abbas KhosraviAbbas Khosravi, Douglas CreightonDouglas Creighton, Imali HettiarachchiImali HettiarachchiAn 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.
History
Event
International Joint Conference on Neural Networks (2015: Killarney, Ireland)Volume
2015-SeptemberPagination
1 - 8Publisher
IEEELocation
Killarney, IrelandPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2015-07-12End date
2015-07-17ISBN-13
9781479919604Language
engPublication classification
E Conference publication; E1 Full written paper - refereedCopyright notice
2015, IEEETitle of proceedings
IJCNN 2015: Proceedings of the 2015 International Joint Conference on Neural NetworksUsage metrics
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No categories selectedKeywords
Science & TechnologyTechnologyComputer Science, Artificial IntelligenceComputer Science, Hardware & ArchitectureEngineering, Electrical & ElectronicComputer ScienceEngineeringWavelet transformfuzzy standard additive modelWilcoxon testEEG signal classificationmotor imagery dataBCI COMPETITION 2003FEATURE-EXTRACTIONFEATURE-SELECTIONCLASSIFICATIONPOTENTIALSMACHINESETS
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