Predictability of human EEG: a dynamical approach View Full Text


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Article Info

DATE

1991-03

AUTHORS

D. Gallez, A. Babloyantz

ABSTRACT

The electroencephalogram recordings from human scalp are analysed in the framework of recent methods of nonlinear dynamics. Three stages of brain activity are considered: the alpha waves (eyes closed), the deep sleep (stage four) and the Creutzfeld-Jakob coma. Two dynamical parameters of the attractors are evaluated. These are the Lyapunov exponents, which measure the divergence or convergence of trajectories in phase space and the Kolmogorov or metric entropy, whose inverse gives the mean predicting time of a given EEG signal. In all the stages considered, the results reveal the presence of at least two positive Lyapunov exponents, which are the footprints of chaos. This number increases to three positive exponents in the case of alpha waves, indicating that although for very short episodes the alpha waves seem extremely coherent, the variability of the brain increases markedly over larger periods of activity. The degree of entropy/chaos increases from coma to deep sleep and then to alpha waves. The large predicting time observed for deep sleep suggests that these waves are related to a slow rate of information processing. The predicting time of the alpha waves is much smaller, indicating a rapid loss of information. Finally, with the help of the Lyapunov exponents, the attractor's dimensions are evaluated using two different conjectures and compared to values obtained previously by the Grassberger-Procaccia algorithm. More... »

PAGES

381-391

References to SciGraph publications

  • 1989. Some Remarks on Nonlinear Data Analysis of Physiological Time Series in MEASURES OF COMPLEXITY AND CHAOS
  • 1987-12. Uncertainty analysis of human EEG spectra: A multivariate information theoretical method for the analysis of brain activity in BIOLOGICAL CYBERNETICS
  • 1988. The EEG is Not a Simple Noise: Strange Attractors in Intracranial Structures in DYNAMICS OF SENSORY AND COGNITIVE PROCESSING BY THE BRAIN
  • 1977-12. Spatiotemporal patterns in epileptic seizures in BIOLOGICAL CYBERNETICS
  • 1987. Strange Attractors in the Human Cortex in TEMPORAL DISORDER IN HUMAN OSCILLATORY SYSTEMS
  • 1988. The Creutzfeld-Jakob Disease in the Hierarchy of Chaotic Attractors in FROM CHEMICAL TO BIOLOGICAL ORGANIZATION
  • 1987-05. Simulation of chaotic EEG patterns with a dynamic model of the olfactory system in BIOLOGICAL CYBERNETICS
  • 1979. Numerical solution of a generalized eigenvalue problem for even mappings in FUNCTIONAL DIFFERENTIAL EQUATIONS AND APPROXIMATION OF FIXED POINTS
  • 1987. Data Requirements for Reliable Estimation of Correlation Dimensions in CHAOS IN BIOLOGICAL SYSTEMS
  • 1986. Problems Associated with Dimensional Analysis of Electroencephalogram Data in DIMENSIONS AND ENTROPIES IN CHAOTIC SYSTEMS
  • 1979-03. Self-organization in biological systems with multiple cellular contacts in BULLETIN OF MATHEMATICAL BIOLOGY
  • 1989-11. Evidence of chaotic dynamics underlying the human alpha-rhythm electroencephalogram in BIOLOGICAL CYBERNETICS
  • 1979. Chaotic behavior of multidimensional difference equations in FUNCTIONAL DIFFERENTIAL EQUATIONS AND APPROXIMATION OF FIXED POINTS
  • 1981. Detecting strange attractors in turbulence in DYNAMICAL SYSTEMS AND TURBULENCE, WARWICK 1980
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/bf00224705

    DOI

    http://dx.doi.org/10.1007/bf00224705

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1051735406

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/2049414


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