Analysis of EEG data using optimization, statistics, and dynamical system techniques

The use of dynamical system techniques, optimization methods and statistical algorithms to estimate the characteristics of brain electrical activity are explored. A system approach for characterizing EEG (electroencephalogram) signals, based on nonlinear estimation of dynamical characteristics and m...

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Bibliographic Details
Published inComputational statistics & data analysis Vol. 44; no. 1; pp. 391 - 408
Main Authors Pardalos, Panos M., Yatsenko, Vitaliy, Sackellares, J.Chris, Shiau, Deng-Shan, Chaovalitwongse, Wanpracha, Iasemidis, Leonidas D.
Format Journal Article
LanguageEnglish
Published Elsevier B.V 28.10.2003
Elsevier
SeriesComputational Statistics & Data Analysis
Subjects
EEG
Epilepsy
Estimation
Modeling
Optimization
Reconstruction
Statistics
System analysis
Reconstruction
EEG
Epilepsy
Estimation
System analysis
Statistics
Modeling
Optimization
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Summary:The use of dynamical system techniques, optimization methods and statistical algorithms to estimate the characteristics of brain electrical activity are explored. A system approach for characterizing EEG (electroencephalogram) signals, based on nonlinear estimation of dynamical characteristics and modeling the evolution of dynamical processes over time is applied. The dynamical characteristics can be used to better visualize the “state vector” of epileptic EEG signals and for the purpose of pattern recognition. An optimization method for reconstructing parameter spaces of dynamical systems is applied to systems with one or more hidden variables, and can be used to reconstruct maps or differential equations of the brain dynamics. The methods are illustrated by using numerically generated data and EEG data from epileptic patients.
ISSN:0167-9473
1872-7352
DOI:10.1016/S0167-9473(03)00027-6