Abstract
In this paper, autoregressive modeling technique and neural network based modeling techniques are used to model and simulate electroencephalogram (EEG) signals. EEG signal modeling is used as a tool to identify pathophysiological EEG changes potentially useful in clinical diagnosis. The normal, background and epileptic EEG signals are modeled and the dynamical properties of the actual and modeled signals are compared. Chaotic invariants like correlation dimension (D <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2 </sub> ), largest Lyapunov exponent (lambda <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , Hurst exponent (H) and Kolmogorov entropy (K) are used to characterize the dynamical properties of the actual and modeled signals. Our study showed that the dynamical properties of the EEG signal modeled using neural network (NN) techniques are very similar to that of the signal
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