Localized measures for nonstationary time-series of physiological data.
We will discuss localized measures related to the concepts of dimension, Lyapunove exponents (entropy), and recurrence diagrams. We stress the relevance of localized events and coincidences in physiological time series that often are lost when statistical averaging methods are applied. We suggest event-based statistics as an alternative to spectral or averaged-based statistics. The use of wavelets bases for characterizing localized structures is discussed as a potential alternative to Fourier-based analysis. Finally we mention how local domains in state space could be applied as triggers for external stimuli and thereby improve the statistics of ERP recordings.
Mayer-Kress
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Beckman Institute, University of Illinois, Urbana 61801.
D000818:Animals; D001143:Arousal / Q000502:physiology*; D001921:Brain / Q000502:physiology*; D003627:Data Interpretation, Statistical; D004569:Electroencephalography / Q000706:statistics & numerical data*; D006801:Humans; D008959:Models, Neurological; D017711:Nonlinear Dynamics; D012815:Signal Processing, Computer-Assisted; D009435:Synaptic Transmission / Q000502:physiology*