A phenomenological model of EEG based on the dynamics of a stochastic Duffing-van der Pol oscillator network

•EEG can be represented as the output of Duffing-van der Pol oscillator model.•Eyes-closed and eyes-open EEG data are distinct realizations of the model.•Stochastic models are better in terms of power spectra and information entropy.•Model may be used for characterization of distinct brain states an...

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Bibliographic Details
Published in:Biomedical signal processing and control Vol. 15; pp. 1 - 10
Main Authors: Ghorbanian, P., Ramakrishnan, S., Whitman, A., Ashrafiuon, H.
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.01.2015
Subjects:
EEG
Nonlinear oscillator networks
Stochastic dynamics
EEG
Stochastic dynamics
Nonlinear oscillator networks
ISSN:1746-8094
Online Access:Get full text
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Summary:•EEG can be represented as the output of Duffing-van der Pol oscillator model.•Eyes-closed and eyes-open EEG data are distinct realizations of the model.•Stochastic models are better in terms of power spectra and information entropy.•Model may be used for characterization of distinct brain states and disorders. In this work, we propose a novel phenomenological model of the EEG signal based on the dynamics of a coupled Duffing-van der Pol oscillator network. An optimization scheme is adopted to match data generated from the model with clinically obtained EEG data from subjects under resting eyes-open (EO) and eyes-closed (EC) conditions. It is shown that a coupled system of two Duffing-van der Pol oscillators with optimized parameters yields signals with characteristics that match those of the EEG in both the EO and EC cases. The results, which are reinforced using statistical analysis, show that the EEG recordings under EC and EO resting conditions are clearly distinct realizations of the same underlying model occurring due to parameter variations with qualitatively different nonlinear dynamic characteristics. In addition, the interplay between noise and nonlinearity is addressed and it is shown that, for appropriately chosen values of noise intensity in the model, very good agreement exists between the model output and the EEG in terms of the power spectrum as well as Shannon entropy. In summary, the results establish that an appropriately tuned stochastic coupled nonlinear oscillator network such as the Duffing-van der Pol system could provide a useful framework for modeling and analysis of the EEG signal. In turn, design of algorithms based on the framework has the potential to positively impact the development of novel diagnostic strategies for brain injuries and disorders.
ISSN:1746-8094
DOI:10.1016/j.bspc.2014.08.013