<?xml version="1.0" encoding="UTF-8"?> <article key="pdf/6430" mdate="2007-02-27 00:00:00"> <author>Elif Derya UBEYLI and Inan GULER</author> <title>Statistics over Lyapunov Exponents for Feature Extraction Electroencephalographic Changes Detection Case</title> <pages>134 - 137</pages> <year>2007</year> <volume>1</volume> <number>2</number> <journal>International Journal of Psychological and Behavioral Sciences</journal> <ee>https://publications.waset.org/pdf/6430</ee> <url>https://publications.waset.org/vol/2</url> <publisher>World Academy of Science, Engineering and Technology</publisher> <abstract>A new approach based on the consideration that electroencephalogram (EEG) signals are chaotic signals was presented for automated diagnosis of electroencephalographic changes. This consideration was tested successfully using the nonlinear dynamics tools, like the computation of Lyapunov exponents. This paper presented the usage of statistics over the set of the Lyapunov exponents in order to reduce the dimensionality of the extracted feature vectors. Since classification is more accurate when the pattern is simplified through representation by important features, feature extraction and selection play an important role in classifying systems such as neural networks. Multilayer perceptron neural network (MLPNN) architectures were formulated and used as basis for detection of electroencephalographic changes. Three types of EEG signals (EEG signals recorded from healthy volunteers with eyes open, epilepsy patients in the epileptogenic zone during a seizurefree interval, and epilepsy patients during epileptic seizures) were classified. The selected Lyapunov exponents of the EEG signals were used as inputs of the MLPNN trained with Levenberg Marquardt algorithm. The classification results confirmed that the proposed MLPNN has potential in detecting the electroencephalographic changes. </abstract> <index>Open Science Index 2, 2007</index> </article>