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Re-examination of the evidence for low-dimensional, nonlinear structure in the human electroencephalogram
Journal article

Re-examination of the evidence for low-dimensional, nonlinear structure in the human electroencephalogram

James Theiler and Paul E. Rapp
Electroencephalography and clinical neurophysiology, v 98(3), pp 213-222
1996
PMID: 8631281

Abstract

Chaos Dimension Discriminant analysis EEG Nonlinear dynamics Spectral analysis Surrogate data
We have re-examined single channel EEG data obtained from normal human subjects. In the original analysis, calculation of the correlation dimension with the Grassberger-Procaccia algorithm produced results consistent with an interpretation of low-dimensional behavior. The re-examination suggests that the previous indication of low-dimensional structure was an artifact of autocorrelation in the oversampled signal. Calculations with a variant of the Grassberger-Procaccia algorithm modified to be less sensitive to autocorrelations, and comparison with linear gaussian surrogate data, indicate that these data may be more appropriately modeled by linearly filtered noise. Discriminant analysis further indicates that the correlation dimension is a poor discriminator for distinguishing between EEGs recorded at rest and during periods of cognitive activity. It remains possible that the application of other nonlinear measures or the examination of multichannel EEG data may resolve structures not found in these calculations.

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Collaboration types
Domestic collaboration
Web of Science research areas
Clinical Neurology
Engineering, Biomedical
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