Comparison of wavelets for classification of cognitive EEG signals

Author

Eraldemir, S. Goksel ; Yildirim, Esen

Author_Institution

Iskenderun Meslek Yuksekokulu, Mustafa Kemal Univ., Hatay, Turkey

fYear

2015

fDate

16-19 May 2015

Firstpage

1381

Lastpage

1384

Abstract

In 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.

Keywords

cognition; discrete wavelet transforms; electroencephalography; feature extraction; medical signal processing; signal classification; BayesNet classifiers; Bior wavelet types; Coiflet wavelet types; EEG signal analysis; J48 classifiers; Symlet wavelet types; classification performances; cognitive EEG signal classification; discrete wavelet transform; feature extraction; math processing; text reading; Classification algorithms; Computer science; Discrete wavelet transforms; Electroencephalography; Wavelet analysis; Biorthogonal; Coiflet; Discrete Wavelet Transform; EEG Classification; Symlet;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing and Communications Applications Conference (SIU), 2015 23th

Conference_Location

Malatya

Type

conf

DOI

10.1109/SIU.2015.7130099

Filename

7130099