EEG simulation by 2D interconnected chaotic oscillators

Adam Kubany; Ziv Mhabary; Vladimir Gontar

doi:10.1016/j.chaos.2010.10.001

EEG simulation by 2D interconnected chaotic oscillators

Adam Kubany
, Ziv Mhabary
, Vladimir Gontar

Department of Industrial Engineering and Management

Research output: Contribution to journal › Article › peer-review

Abstract

An artificial neuronal network composed by 2D interconnected chaotic oscillators is explored for brain waves (EEG) simulation. For the inverse problem solution a parallel real-coded genetic algorithm (PRCGA) is proposed. In order to conduct thorough comparison between the simulated and target signal characteristics, a spectrum analysis of the signals is undertaken. A good matching between the theoretical and experimental EEG signals has been achieved. Numerical results of calculations are presented and discussed.

Original language	English
Pages (from-to)	1-8
Number of pages	8
Journal	Chaos, Solitons and Fractals
Volume	44
Issue number	1-3
DOIs	https://doi.org/10.1016/j.chaos.2010.10.001
State	Published - 1 Jan 2011

ASJC Scopus subject areas

Statistical and Nonlinear Physics
Mathematical Physics
General Physics and Astronomy
Applied Mathematics

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10.1016/j.chaos.2010.10.001

Cite this

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title = "EEG simulation by 2D interconnected chaotic oscillators",

abstract = "An artificial neuronal network composed by 2D interconnected chaotic oscillators is explored for brain waves (EEG) simulation. For the inverse problem solution a parallel real-coded genetic algorithm (PRCGA) is proposed. In order to conduct thorough comparison between the simulated and target signal characteristics, a spectrum analysis of the signals is undertaken. A good matching between the theoretical and experimental EEG signals has been achieved. Numerical results of calculations are presented and discussed.",

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AB - An artificial neuronal network composed by 2D interconnected chaotic oscillators is explored for brain waves (EEG) simulation. For the inverse problem solution a parallel real-coded genetic algorithm (PRCGA) is proposed. In order to conduct thorough comparison between the simulated and target signal characteristics, a spectrum analysis of the signals is undertaken. A good matching between the theoretical and experimental EEG signals has been achieved. Numerical results of calculations are presented and discussed.

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EEG simulation by 2D interconnected chaotic oscillators

Abstract

ASJC Scopus subject areas

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