Overview

This book provides a thorough review of a class of powerful algorithms for the numerical analysis of complex time series data which were obtained from dynamical systems. These algorithms are based on the concept of state space representations of the underlying dynamics, as introduced by nonlinear dynamics. In particular, current algorithms for state space reconstruction, correlation dimension estimation, testing for determinism and surrogate data testing are presented algorithms which have been playing a central role in the investigation of deterministic chaos and related phenomena since 1980. Special emphasis is given to the much-disputed issue whether these algorithms can be successfully employed for the analysis of the human electroencephalogram.Contents: Dynamical Systems, Time Series and AttractorsLinear MethodsState Space Reconstruction: Theoretical FoundationsState Space Reconstruction: Practical ApplicationDimensions: Basic DefinitionsLyapunov Exponents and EntropiesNumerical Estimation of the Correlation DimensionSources of Error and Data Set Size RequirementsMonte Carlo Analysis of Dimension EstimationSurrogate Data TestsDimension Analysis of the Human EEGTesting for Determinism in Time SeriesReadership: Graduates and scientists in physics, applied mathematics, neurology, theoretical biology, economics, meteorology and neuroinformatics.

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9781299614932

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