Eraldemir, S. GokselYildirim, Esen2024-09-182024-09-182015978-1-4673-7386-92165-0608https://hdl.handle.net/20.500.12483/1246223nd Signal Processing and Communications Applications Conference (SIU) -- MAY 16-19, 2015 -- Inonu Univ, Malatya, TURKEYIn this work, different wavelet types, that have been frequently used in EEG signal analysis and classification, are compared for cognitive EEG classification. EEG signals are collected from 18 healthy subjects during math processing and simple text reading. Symlet, coiflet and bior wavelet types are used for feature extraction and classification performances of BayesNet and J48 classifiers are compared. The best true positive rate of 90.6% is obtained using Boir 2.4 wavelet type with J48 classifier.trinfo:eu-repo/semantics/closedAccessSymletCoifletBiorthogonalDiscrete Wavelet TransformEEG ClassificationConference Object138113842-s2.0-84939142408N/AWOS:000380500900326N/A