Nonlinear models of electroencephalographic dynamics
J.R Carbo, M Leganoa, Jorge A. González · Revista Mexicana de Física · 1994
Alternative nonlinear techniques to the characterization of EEG time series are pro- posed, which a110wtheir modeling and the prediction of bifurcations due to changes of parameters. These techniques show clear-cut differences between normal and pathological states which can be used as diagnostic tools to the evaluation of patient's normality. We also formulate a new homeodynamical principie of health. Recent years have witnessed an increase of the interest in the application of non linear concepts in the analysis of brain activity, mainly through the analysis of the evolution and dynamics of the electroencephalographic (EEG) recordings (measurements of potential differences between fixed points in the scalp of the subject vs. time). Most of the work has been dedicated to the use of the embedding theorem and the calculation of the fractal dimension of the EEG time series (1-8). In this paper we propose the use of other alternative non linear techniques to char- acterize EEG signals. \Ve will also construct sorne analytical models to describe EEG dynamics, which will allow us not only to reproduce sorne characteristics of the real time series, but also to predict the appearance of bifurcations under variations of parameters. Finally it will be formulated a new homeodynamical principIe to describe the healthy state of the individual.