TY - GEN
T1 - Nonlinear dynamic analysis of EEG using a stochastic duffing-van der Pol oscillator model
AU - Ghorbanian, Parham
AU - Ramakrishnan, Subramanian
AU - Whitman, Alan
AU - Ashrafiuon, Hashem
PY - 2014/1/1
Y1 - 2014/1/1
N2 - 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.
AB - 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.
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U2 - 10.1115/DSCC2014-5854
DO - 10.1115/DSCC2014-5854
M3 - Conference contribution
AN - SCOPUS:84929254261
T3 - ASME 2014 Dynamic Systems and Control Conference, DSCC 2014
BT - 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
PB - American Society of Mechanical Engineers
T2 - ASME 2014 Dynamic Systems and Control Conference, DSCC 2014
Y2 - 22 October 2014 through 24 October 2014
ER -