Volume 84, Number 1, October 2008
|Number of page(s)||6|
|Published online||19 September 2008|
Estimating the strength of genuine and random correlations in non-stationary multivariate time series
Facultad de Ciencias, Universidad Autónoma del Estado de Morelos - 62209 Cuernavaca, México
2 Max-Planck-Institut für Physik komplexer Systeme - D-01187 Dresden, Germany, EU
3 Manchester Interdisciplinary Biocentre, University of Manchester - Manchester M1 7DN, UK, EU
4 Department of Neurology, Inselspital, Bern University Hospital, and University of Bern - Switzerland
Corresponding author: firstname.lastname@example.org
Accepted: 25 August 2008
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: 05.45.Tp – Time series analysis / 89.75.Fb – Structures and organization in complex systems / 87.19.L- – Neuroscience
© EPLA, 2008
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