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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Published in | Computational statistics & data analysis Vol. 44; no. 1; pp. 391 - 408 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
Elsevier B.V
28.10.2003
Elsevier |
Series | Computational Statistics & Data Analysis |
Subjects | |
Online Access | Get full text |
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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. |
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ISSN: | 0167-9473 1872-7352 |
DOI: | 10.1016/S0167-9473(03)00027-6 |