Description

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.

Topics In Nonlinear Time Series Analysis, With Implications For Eeg Analysis

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Hardback by Andreas Galka

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This book provides a thorough review of a class of powerful algorithms for the numerical analysis of complex time series... Read more

    Publisher: World Scientific Publishing Co Pte Ltd
    Publication Date: 21/02/2000
    ISBN13: 9789810241483, 978-9810241483
    ISBN10: 9810241488

    Number of Pages: 360

    Non Fiction , Mathematics & Science , Education

    Description

    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.

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