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Analysis of Strange Attractors in EEGs with Kinesthetic Experience and 4-D Computer Graphics

  • W. J. Freeman

Abstract

Newly developed techniques for numerical analysis of the time series derived from physical and chemical systems displaying turbulence and other seemingly random behavior have recently been applied to electroencephalographic potentials (EEGs). Estimates for the lower bounds of measures of the fractal dimension, Hausdorff dimension, Lyapunov exponents, Kolmogorov entropy, and other related coefficients have indicated that by suitable means of observation the background “noise” generated by brains may be nonrandom, and may be constrained by as yet undefined aspects of neural mechanisms generating it. In a word, brain activity, at least in some states and in some brain parts, appears to be chaotic and not stochastic (Rapp et al. 1985; Freeman and Viana Di Prisco 1986; Babloyantz and Destexhe 1986; Havstadt and Ehlers 1987; Meyer-Kress 1987; Freeman 1987 a, b, 1988).

Keywords

Fractal Dimension Lyapunov Exponent Olfactory Bulb Chaotic Attractor Strange Attractor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© Springer-Verlag Berlin Heidelberg 1990

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  • W. J. Freeman

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