Stochastic oscillator model of EEG based on information content and complexity

Parham Ghorbanian; Subramanian Ramakrishnan; Hashem Ashrafiuon

doi:10.1115/DSCC2014-5929

Stochastic oscillator model of EEG based on information content and complexity

Parham Ghorbanian, Subramanian Ramakrishnan, Hashem Ashrafiuon

Mechanical & Industrial Engineering

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

Abstract

In this study, a stochastic Duffing-van der Pol coupled two oscillator system is designed to produce output matching the information content, complexity measure, and frequency content of actual electroencephalography (EEG) signals. This is achieved by deriving the oscillator model parameters and noise intensity using an optimization scheme whose objective is to minimize a weighed average of errors in sample entropy, Shannon entropy, and powers of the major brain frequency bands. The signals produced by the optimal model are then compared with the EEG signal using phase portrait reconstruction. The study shows that the model can effectively reproduce signals thatmatch EEG recorded under different brain states with respect to multiple metrics.

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/DSCC2014-5929
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.

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Ghorbanian, P., Ramakrishnan, S., & Ashrafiuon, H. (2014). Stochastic oscillator model of EEG based on information content and complexity. 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 5929 (ASME 2014 Dynamic Systems and Control Conference, DSCC 2014; Vol. 2). American Society of Mechanical Engineers. https://doi.org/10.1115/DSCC2014-5929

Stochastic oscillator model of EEG based on information content and complexity. / Ghorbanian, Parham; Ramakrishnan, Subramanian; Ashrafiuon, Hashem.
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. 5929 (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 & Ashrafiuon, H 2014, Stochastic oscillator model of EEG based on information content and complexity. 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., 5929, 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/DSCC2014-5929

Ghorbanian P, Ramakrishnan S, Ashrafiuon H. Stochastic oscillator model of EEG based on information content and complexity. 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. 5929. (ASME 2014 Dynamic Systems and Control Conference, DSCC 2014). doi: 10.1115/DSCC2014-5929

Ghorbanian, Parham ; Ramakrishnan, Subramanian ; Ashrafiuon, Hashem. / Stochastic oscillator model of EEG based on information content and complexity. 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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Stochastic oscillator model of EEG based on information content and complexity

Abstract

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