Rummel, Christian; Müller, Markus; Baier, Gerold; Amor, Frédérique; Schindler, Kaspar (2010). Analyzing spatio-temporal patterns of genuine cross-correlations. Journal of neuroscience methods, 191(1), pp. 94-100. Amsterdam: Elsevier 10.1016/j.jneumeth.2010.05.022
Full text not available from this repository.In multivariate time series analysis, the equal-time cross-correlation is a classic and computationally efficient measure for quantifying linear interrelations between data channels. When the cross-correlation coefficient is estimated using a finite amount of data points, its non-random part may be strongly contaminated by a sizable random contribution, such that no reliable conclusion can be drawn about genuine mutual interdependencies. The random correlations are determined by the signals' frequency content and the amount of data points used. Here, we introduce adjusted correlation matrices that can be employed to disentangle random from non-random contributions to each matrix element independently of the signal frequencies. Extending our previous work these matrices allow analyzing spatial patterns of genuine cross-correlation in multivariate data regardless of confounding influences. The performance is illustrated by example of model systems with known interdependence patterns. Finally, we apply the methods to electroencephalographic (EEG) data with epileptic seizure activity.
Item Type: |
Journal Article (Original Article) |
|---|---|
Division/Institute: |
04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic and Interventional Neuroradiology 04 Faculty of Medicine > Department of Head Organs and Neurology (DKNS) > Clinic of Neurology |
UniBE Contributor: |
Rummel, Christian, Amor, Frédérique, Schindler, Kaspar Anton |
ISSN: |
0165-0270 |
Publisher: |
Elsevier |
Language: |
English |
Submitter: |
Factscience Import |
Date Deposited: |
04 Oct 2013 14:08 |
Last Modified: |
02 Mar 2023 23:20 |
Publisher DOI: |
10.1016/j.jneumeth.2010.05.022 |
PubMed ID: |
20566351 |
Web of Science ID: |
000280973300012 |
URI: |
https://boris.unibe.ch/id/eprint/383 (FactScience: 197892) |
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