Stochastic oscillator model of EEG based on information content and complexity

Parham Ghorbanian, Subramanian Ramakrishnan, Hashem Ashrafiuon

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish (US)
Title of host publicationDynamic 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
PublisherAmerican Society of Mechanical Engineers
Volume2
ISBN (Electronic)9780791846193
DOIs
StatePublished - Jan 1 2014
EventASME 2014 Dynamic Systems and Control Conference, DSCC 2014 - San Antonio, United States
Duration: Oct 22 2014Oct 24 2014

Other

OtherASME 2014 Dynamic Systems and Control Conference, DSCC 2014
CountryUnited States
CitySan Antonio
Period10/22/1410/24/14

Fingerprint

Electroencephalography
Brain
Entropy
Frequency bands

Cite this

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 (Vol. 2). [5929] 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. Vol. 2 American Society of Mechanical Engineers, 2014. 5929.

Research output: Chapter in Book/Report/Conference proceedingConference 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. vol. 2, 5929, 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. Vol. 2. American Society of Mechanical Engineers. 2014. 5929 https://doi.org/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. Vol. 2 American Society of Mechanical Engineers, 2014.
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