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Estimating the strength of genuine and random correlations in non-stationary multivariate time series

M. Müller1,2, G. Baier1,3, C. Rummel4,1 and K. Schindler4

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The estimation of the amount of genuine cross-correlation strength from multivariate data sets is a nontrivial task, especially when the power spectra of the signals vary dynamically. In this case, the amount of random correlations may vary drastically, even when the length T of the data window used for the construction of the zero-lag correlation matrix is kept constant. In the present letter we introduce correlation measures that allow to distinguish quantitatively genuine and random cross-correlations. The measures are carefully tested by employing model data and exemplarily we demonstrate their performance by their application to a clinical electroencephalogram (EEG) of an epilepsy patient.


PACS

89.75.Fb Structures and organization in complex systems

87.19.L- Neuroscience

05.45.Tp Time series analysis

Subjects

Medical physics

Biological physics

Statistical physics and nonlinear systems

Dates

Issue 1 (October 2008)

Received 4 June 2008, accepted for publication 25 August 2008

Published 19 September 2008



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