Genuine cross-correlations: Which surrogate based measure reproduces analytical results best?

Marín García, Arlex Oscar; Müller, Markus Franziskus; Schindler, Kaspar; Rummel, Christian (2013). Genuine cross-correlations: Which surrogate based measure reproduces analytical results best? Neural networks, 46, pp. 154-164. Amsterdam: Elsevier 10.1016/j.neunet.2013.05.009

[img] Text
Schindler_NeuralNetw.pdf - Published Version
Restricted to registered users only
Available under License Publisher holds Copyright.

Download (1MB) | Request a copy

The analysis of short segments of noise-contaminated, multivariate real world data constitutes a challenge. In this paper we compare several techniques of analysis, which are supposed to correctly extract the amount of genuine cross-correlations from a multivariate data set. In order to test for the quality of their performance we derive time series from a linear test model, which allows the analytical derivation of genuine correlations. We compare the numerical estimates of the four measures with the analytical results for different correlation pattern. In the bivariate case all but one measure performs similarly well. However, in the multivariate case measures based on the eigenvalues of the equal-time cross-correlation matrix do not extract exclusively information about the amount of genuine correlations, but they rather reflect the spatial organization of the correlation pattern. This may lead to failures when interpreting the numerical results as illustrated by an application to three electroencephalographic recordings of three patients suffering from pharmacoresistent epilepsy.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Department of Head Organs and Neurology (DKNS) > Clinic of Neurology
04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic and Interventional Neuroradiology

UniBE Contributor:

Schindler, Kaspar Anton and Rummel, Christian

Subjects:

600 Technology > 610 Medicine & health

ISSN:

0893-6080

Publisher:

Elsevier

Language:

English

Submitter:

Factscience Import

Date Deposited:

04 Oct 2013 14:39

Last Modified:

17 Nov 2015 10:11

Publisher DOI:

10.1016/j.neunet.2013.05.009

PubMed ID:

23751366

BORIS DOI:

10.7892/boris.15730

URI:

https://boris.unibe.ch/id/eprint/15730 (FactScience: 223159)

Actions (login required)

Edit item Edit item
Provide Feedback