Estimating the strength of genuine and random correlations in non-stationary multivariate time seriesM. Müller1, 2, G. Baier1, 3, C. Rummel4, 1 and K. Schindler4
1 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
received 4 June 2008; accepted in final form 25 August 2008; published October 2008
published online 19 September 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.
05.45.Tp - Time series analysis.
89.75.Fb - Structures and organization in complex systems.
87.19.L- - Neuroscience.
© EPLA 2008