Stochastic dynamic modeling of the human brain EEG signal

Parham Ghorbanian, Subramanian Ramakrishnan, Adam J. Simon, Hashem Ashrafiuon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

The occurrence and risk of recurrence of brain related injuries and diseases are difficult to characterize due to various factors including inter-individual variability. A useful approach is to analyze the brain electroencephalogram (EEG) for differences in brain frequency bands in the signals obtained from potentially injured and healthy normal subjects. However, significant shortcomings include: (1) contrary to empirical evidence, current spectral signal analysis based methods often assume that the EEG signal is linear and stationary; (2) nonlinear time series analysis methods are mostly numerical and do not possess any predictive features. In this work, we develop models based on stochastic differential equations that can output signals with similar frequency and magnitude characteristics of the brain EEG. Initially, a coupled linear oscillatormodel with a large number of degrees of freedom is developed and shown to capture the characteristics of the EEG signal in the major brain frequency bands. Then, a nonlinear stochastic model based on the Duffing oscillator with far fewer degrees of freedom is developed and shown to produce outputs that can closely match the EEG signal. It is shown that such a compact nonlinear model can provide better insight into EEG dynamics through only few parameters, which is a step towards developing a framework with predictive capabilities for addressing brain injuries.

Original languageEnglish (US)
Title of host publicationControl, Monitoring, and Energy Harvesting of Vibratory Systems; Cooperative and Networked Control; Delay Systems; Dynamical Modeling and Diagnostics in Biomedical Systems;
PublisherAmerican Society of Mechanical Engineers (ASME)
Volume2
ISBN (Print)9780791856130
DOIs
StatePublished - Jan 1 2013
EventASME 2013 Dynamic Systems and Control Conference, DSCC 2013 - Palo Alto, CA, United States
Duration: Oct 21 2013Oct 23 2013

Other

OtherASME 2013 Dynamic Systems and Control Conference, DSCC 2013
CountryUnited States
CityPalo Alto, CA
Period10/21/1310/23/13

Fingerprint

Electroencephalography
Brain
Frequency bands
Time series analysis
Signal analysis
Stochastic models
Differential equations

Cite this

Ghorbanian, P., Ramakrishnan, S., Simon, A. J., & Ashrafiuon, H. (2013). Stochastic dynamic modeling of the human brain EEG signal. In Control, Monitoring, and Energy Harvesting of Vibratory Systems; Cooperative and Networked Control; Delay Systems; Dynamical Modeling and Diagnostics in Biomedical Systems; (Vol. 2). [DSCC2013-3881] American Society of Mechanical Engineers (ASME). https://doi.org/10.1115/DSCC2013-38811

Stochastic dynamic modeling of the human brain EEG signal. / Ghorbanian, Parham; Ramakrishnan, Subramanian; Simon, Adam J.; Ashrafiuon, Hashem.

Control, Monitoring, and Energy Harvesting of Vibratory Systems; Cooperative and Networked Control; Delay Systems; Dynamical Modeling and Diagnostics in Biomedical Systems;. Vol. 2 American Society of Mechanical Engineers (ASME), 2013. DSCC2013-3881.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Ghorbanian, P, Ramakrishnan, S, Simon, AJ & Ashrafiuon, H 2013, Stochastic dynamic modeling of the human brain EEG signal. in Control, Monitoring, and Energy Harvesting of Vibratory Systems; Cooperative and Networked Control; Delay Systems; Dynamical Modeling and Diagnostics in Biomedical Systems;. vol. 2, DSCC2013-3881, American Society of Mechanical Engineers (ASME), ASME 2013 Dynamic Systems and Control Conference, DSCC 2013, Palo Alto, CA, United States, 10/21/13. https://doi.org/10.1115/DSCC2013-38811
Ghorbanian P, Ramakrishnan S, Simon AJ, Ashrafiuon H. Stochastic dynamic modeling of the human brain EEG signal. In Control, Monitoring, and Energy Harvesting of Vibratory Systems; Cooperative and Networked Control; Delay Systems; Dynamical Modeling and Diagnostics in Biomedical Systems;. Vol. 2. American Society of Mechanical Engineers (ASME). 2013. DSCC2013-3881 https://doi.org/10.1115/DSCC2013-38811
Ghorbanian, Parham ; Ramakrishnan, Subramanian ; Simon, Adam J. ; Ashrafiuon, Hashem. / Stochastic dynamic modeling of the human brain EEG signal. Control, Monitoring, and Energy Harvesting of Vibratory Systems; Cooperative and Networked Control; Delay Systems; Dynamical Modeling and Diagnostics in Biomedical Systems;. Vol. 2 American Society of Mechanical Engineers (ASME), 2013.
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