Fractal dimensions of short EEG time series in humans

Hubert Preißl; Werner Lutzenberger; Friedemann Pulvermüller; Niels Birbaumer

doi:10.1016/S0304-3940(97)00192-4

Fractal dimensions of short EEG time series in humans

Hubert Preißl, Werner Lutzenberger, Friedemann Pulvermüller, Niels Birbaumer

Istituto di Neuroriabilitazione Motoria San Camillo

Research output: Contribution to journal › Article › peer-review

Abstract

Fractal dimension has been proposed as a useful measure for the characterisation of electrophysiological time series. But one of the problems of this approach, is the difficulty to record time series long enough to determine the 'real' fractal dimension. Nevertheless it is possible to calculate fractal dimensions for very short data-segments. Using time series of different length it is possible to show, that there is a monotoneous relation between fractal dimension and the number of data-points. This relation could be further interpreted with the help of an extrapolation scheme. In addition this effect is also seen with surrogate data, generated from that signal. We conclude that it is feasible to use fractal dimension as a tool to characterise the complexity for short electroencephalographic (EEG) time series, but it is not possible to decide whether the brain is a chaotic system or not.

Original language	English
Pages (from-to)	77-80
Number of pages	4
Journal	Neuroscience Letters
Volume	225
Issue number	2
DOIs	https://doi.org/10.1016/S0304-3940(97)00192-4
Publication status	Published - Apr 4 1997

Keywords

Chaos
Dimension
Electroencephalogram
Non-linearity
Surrogate data

ASJC Scopus subject areas

Neuroscience(all)

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10.1016/S0304-3940(97)00192-4

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abstract = "Fractal dimension has been proposed as a useful measure for the characterisation of electrophysiological time series. But one of the problems of this approach, is the difficulty to record time series long enough to determine the 'real' fractal dimension. Nevertheless it is possible to calculate fractal dimensions for very short data-segments. Using time series of different length it is possible to show, that there is a monotoneous relation between fractal dimension and the number of data-points. This relation could be further interpreted with the help of an extrapolation scheme. In addition this effect is also seen with surrogate data, generated from that signal. We conclude that it is feasible to use fractal dimension as a tool to characterise the complexity for short electroencephalographic (EEG) time series, but it is not possible to decide whether the brain is a chaotic system or not.",

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