Dimensional analysis of resting human EEG II: Surrogate‐data testing indicates nonlinearity but not low‐dimensional chaos

Publisher Website
Google Scholar
Abstract
Surrogate-data testing has recently been proposed as one way to detect the presence of nonlinearity and low-dimensional chaos in experimental time series. Such testing involves estimating correlation dimension for both the original data and surrogate data from which nonlinearity has been removed. We applied such testing to the same resting, eyes-closed, and eyes-open electroencephalogram (EEG) data set that was originally analyzed using dimension estimation applied only to the original data (Pritchard & Duke, 1992). Two kinds of surrogate-data sets had higher estimated dimension and poorer saturation. This indicates that the normal resting human EEG is nonlinear and therefore not a linear-stochastic system. Because nearly complete saturation at some loci was not differently affected by the surrogate-data procedures, our results also indicate that the normal resting human EEG is high dimensional and does not represent low-dimensional chaos.