The EEG signals are highly subjective and the information about the
various states may appear at random in the time scale. For example,
a time series may be obtained by recording at regular time
intervals the mean electrical activity of a portion of the
mammalian brain. More specifically, by using a time series one can
determine the possibility of constructing an attractor and thereby
establishing the deterministic character of dynamic underlying
system. Such methods from the non linear dynamical theory can be
dragged for better perception of EEG signals. The complexity of
drowsiness estimation and characterizing the EEG signals can be
brought under some chaotic optimization techniques. Chapter1
introduces Chaos, Non linear dynamics and focus of the research.
Chapter 2 discusses the literature survey of correlation dimension
estimation. Chapter 3 and Chapter 4 enumerate the review of LAB
view and Mat lab software for the book. Results are discussed in
Chapter 5. Chapter 6 brings out the conclusion of this work. Future
scope of this work is solemnized in chapter 7. This monograph is
useful for all Engineering undergraduate, graduates students and
practicing engineers.
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