Nonlinear dynamic analysis of EEG using a stochastic duffing-van der Pol oscillator model

Parham Ghorbanian; Subramanian Ramakrishnan; Alan Whitman; Hashem Ashrafiuon

doi:10.1115/DSCC2014-5854

Nonlinear dynamic analysis of EEG using a stochastic duffing-van der Pol oscillator model

Parham Ghorbanian, Subramanian Ramakrishnan, Alan Whitman, Hashem Ashrafiuon

Mechanical & Industrial Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

Abstract

In this work, we model electroencephalography (EEG) signals as the stochastic output of a double Duffing-van der Pol oscillator networks. We develop a novel optimization scheme to match data generated from the model with clinically obtained EEG data from subjects under resting eyes-open (EO) and eyesclosed (EC) conditions and derive models with outputs that show very good agreement with EEG signals in terms of both frequency and information contents. The results, reinforced by statistical analysis, shows that the EEG recordings under EC and EO resting conditions are distinct realizations of the same underlying model occurring due to parameter variations. Furthermore, the EC and EO EEG signals each exhibit distinct nonlinear dynamic characteristics. In summary, it is established that the stochastic coupled nonlinear oscillator network can provide a useful framework for modeling and analysis of EEG signals that are recorded under variety of conditions.

Original language	English (US)
Title of host publication	Dynamic Modeling and Diagnostics in Biomedical Systems; Dynamics and Control of Wind Energy Systems; Vehicle Energy Management Optimization; Energy Storage, Optimization; Transportation and Grid Applications; Estimation and Identification Methods, Tracking, Detection, Alternative Propulsion Systems; Ground and Space Vehicle Dynamics; Intelligent Transportation Systems and Control; Energy Harvesting; Modeling and Control for Thermo-Fluid Applications, IC Engines, Manufacturing
Publisher	American Society of Mechanical Engineers
ISBN (Electronic)	9780791846193
DOIs	https://doi.org/10.1115/DSCC2014-5854
State	Published - 2014
Event	ASME 2014 Dynamic Systems and Control Conference, DSCC 2014 - San Antonio, United States Duration: Oct 22 2014 → Oct 24 2014

Publication series

Name	ASME 2014 Dynamic Systems and Control Conference, DSCC 2014
Volume	2

Other

Other	ASME 2014 Dynamic Systems and Control Conference, DSCC 2014
Country/Territory	United States
City	San Antonio
Period	10/22/14 → 10/24/14

Bibliographical note

Publisher Copyright:
Copyright © 2014 by ASME.

Publisher link

10.1115/DSCC2014-5854

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Ghorbanian, P., Ramakrishnan, S., Whitman, A., & Ashrafiuon, H. (2014). Nonlinear dynamic analysis of EEG using a stochastic duffing-van der Pol oscillator model. In Dynamic Modeling and Diagnostics in Biomedical Systems; Dynamics and Control of Wind Energy Systems; Vehicle Energy Management Optimization; Energy Storage, Optimization; Transportation and Grid Applications; Estimation and Identification Methods, Tracking, Detection, Alternative Propulsion Systems; Ground and Space Vehicle Dynamics; Intelligent Transportation Systems and Control; Energy Harvesting; Modeling and Control for Thermo-Fluid Applications, IC Engines, Manufacturing Article 5854 (ASME 2014 Dynamic Systems and Control Conference, DSCC 2014; Vol. 2). American Society of Mechanical Engineers. https://doi.org/10.1115/DSCC2014-5854

Nonlinear dynamic analysis of EEG using a stochastic duffing-van der Pol oscillator model. / Ghorbanian, Parham; Ramakrishnan, Subramanian; Whitman, Alan et al.
Dynamic Modeling and Diagnostics in Biomedical Systems; Dynamics and Control of Wind Energy Systems; Vehicle Energy Management Optimization; Energy Storage, Optimization; Transportation and Grid Applications; Estimation and Identification Methods, Tracking, Detection, Alternative Propulsion Systems; Ground and Space Vehicle Dynamics; Intelligent Transportation Systems and Control; Energy Harvesting; Modeling and Control for Thermo-Fluid Applications, IC Engines, Manufacturing. American Society of Mechanical Engineers, 2014. 5854 (ASME 2014 Dynamic Systems and Control Conference, DSCC 2014; Vol. 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Ghorbanian, P, Ramakrishnan, S, Whitman, A & Ashrafiuon, H 2014, Nonlinear dynamic analysis of EEG using a stochastic duffing-van der Pol oscillator model. in Dynamic Modeling and Diagnostics in Biomedical Systems; Dynamics and Control of Wind Energy Systems; Vehicle Energy Management Optimization; Energy Storage, Optimization; Transportation and Grid Applications; Estimation and Identification Methods, Tracking, Detection, Alternative Propulsion Systems; Ground and Space Vehicle Dynamics; Intelligent Transportation Systems and Control; Energy Harvesting; Modeling and Control for Thermo-Fluid Applications, IC Engines, Manufacturing., 5854, ASME 2014 Dynamic Systems and Control Conference, DSCC 2014, vol. 2, American Society of Mechanical Engineers, ASME 2014 Dynamic Systems and Control Conference, DSCC 2014, San Antonio, United States, 10/22/14. https://doi.org/10.1115/DSCC2014-5854

Ghorbanian P, Ramakrishnan S, Whitman A, Ashrafiuon H. Nonlinear dynamic analysis of EEG using a stochastic duffing-van der Pol oscillator model. In Dynamic Modeling and Diagnostics in Biomedical Systems; Dynamics and Control of Wind Energy Systems; Vehicle Energy Management Optimization; Energy Storage, Optimization; Transportation and Grid Applications; Estimation and Identification Methods, Tracking, Detection, Alternative Propulsion Systems; Ground and Space Vehicle Dynamics; Intelligent Transportation Systems and Control; Energy Harvesting; Modeling and Control for Thermo-Fluid Applications, IC Engines, Manufacturing. American Society of Mechanical Engineers. 2014. 5854. (ASME 2014 Dynamic Systems and Control Conference, DSCC 2014). doi: 10.1115/DSCC2014-5854

Ghorbanian, Parham ; Ramakrishnan, Subramanian ; Whitman, Alan et al. / Nonlinear dynamic analysis of EEG using a stochastic duffing-van der Pol oscillator model. Dynamic Modeling and Diagnostics in Biomedical Systems; Dynamics and Control of Wind Energy Systems; Vehicle Energy Management Optimization; Energy Storage, Optimization; Transportation and Grid Applications; Estimation and Identification Methods, Tracking, Detection, Alternative Propulsion Systems; Ground and Space Vehicle Dynamics; Intelligent Transportation Systems and Control; Energy Harvesting; Modeling and Control for Thermo-Fluid Applications, IC Engines, Manufacturing. American Society of Mechanical Engineers, 2014. (ASME 2014 Dynamic Systems and Control Conference, DSCC 2014).

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abstract = "In this work, we model electroencephalography (EEG) signals as the stochastic output of a double Duffing-van der Pol oscillator networks. We develop a novel optimization scheme to match data generated from the model with clinically obtained EEG data from subjects under resting eyes-open (EO) and eyesclosed (EC) conditions and derive models with outputs that show very good agreement with EEG signals in terms of both frequency and information contents. The results, reinforced by statistical analysis, shows that the EEG recordings under EC and EO resting conditions are distinct realizations of the same underlying model occurring due to parameter variations. Furthermore, the EC and EO EEG signals each exhibit distinct nonlinear dynamic characteristics. In summary, it is established that the stochastic coupled nonlinear oscillator network can provide a useful framework for modeling and analysis of EEG signals that are recorded under variety of conditions.",

author = "Parham Ghorbanian and Subramanian Ramakrishnan and Alan Whitman and Hashem Ashrafiuon",

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Nonlinear dynamic analysis of EEG using a stochastic duffing-van der Pol oscillator model

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