Title :
A comparison of PCA, ICA and LDA in EEG signal classification using SVM
Author :
M. Ismail Gursoy;Abdulhamit Subast
Author_Institution :
Kahramanmara? S?t?? ?mam ?niversitesi, Elektrik- Elektronik M?hendisli?i, Turkey
fDate :
4/1/2008 12:00:00 AM
Abstract :
Since EEG is one of the most important sources of information in diagnosis of epilepsy, several researchers tried to address the issue of decision support for such a data. We present a method for classifying epilepsy of full spectrum EEG recordings. In the proposed method, autoregressive (AR) model is used to acquire power spectrum of EEG signals, then dimension of the extracted feature vectors is reduced by using ICA, PCA and LDA, and these vectors used as an input to a support vector machine (SVM) with two discrete outputs: epileptic seizure or not. By identifying features in the signal we want to provide an automatic system that will support a physician in the diagnosing process. It is observed that, SVM classification of EEG signals gives better results and these results can also be used for diagnosis of diseases.
Keywords :
"Electroencephalography","Support vector machines","Brain modeling","Brain models","Feature extraction","Principal component analysis","Wavelet analysis"
Conference_Titel :
Signal Processing, Communication and Applications Conference, 2008. SIU 2008. IEEE 16th
Print_ISBN :
978-1-4244-1998-2
DOI :
10.1109/SIU.2008.4632748