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Application of KIII Model to EEG Classification Based on Nonlinear Dynamic Methods

Jin Zhang, Ying Wang, Rulong Wang


Based on the biological olfactory systems, a chaotic neural network, KIII model, was proposed by Prof. Walter J. Freeman. KIII model not only can simulate the output waveforms in electroencephalogram (EEG), but also has the capability of pattern recognition. Based on nonlinear dynamic methods, two nonlinear dynamics indexes, approximate entropies (ApEn) and Lyapunov exponents, are used to extract EEG feature. KIII model is used to recognize hypoxia EEG. Experimental results show that (1) ApEn and Lyapunov exponents can denote the characteristics of EEG effectively; (2) KIII model has good performance to recognize the nonlinear signals.


KIII model, EEG, Lyapunov exponents, ApEn

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