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Non-gaussianity and non-linearity in electroencephalographic time series

  • Th. Gasser
  • G. Dumermuth
Part II: Research Reports
Part of the Lecture Notes in Control and Information Sciences book series (LNCIS, volume 16)

Abstract

In this interdisciplinary contribution we discuss a number of problems related to stochastic models and statistical methods used in electroencephalography. The main part of the paper is devoted to the assumption of a Gaussian process. We present a variety of methods to check empirically such an assumption, together with examples. The deviations from a Gaussian process which occur in EEG analysis are interpreted in terms of non-linear dynamics; the input-output-map is assumed to be well represented by a Volterra series.

Keywords

Gaussian Process Volterra Series Inverse Filter High Order Spectrum Complex Demodulation 
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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Copyright information

© Springer-Verlag 1979

Authors and Affiliations

  • Th. Gasser
    • 1
  • G. Dumermuth
    • 1
  1. 1.Universitäts-KinderklinikZürich

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