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Bivariate surrogate techniques: Necessity, strengths, and caveats

Andrzejak, RG; Kraskov, A; Stogbauer, H; Mormann, F; Kreuz, T; (2003) Bivariate surrogate techniques: Necessity, strengths, and caveats. PHYS REV E , 68 (6) , Article 066202. 10.1103/PhysRevE.68.066202.

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Abstract

The concept of surrogates allows testing results from time series analysis against specified null hypotheses. In application to bivariate model dynamics we here compare different types of surrogates, each designed to test against a different null hypothesis, e.g., an underlying bivariate linear stochastic process. Two measures that aim at a characterization of interdependence between nonlinear deterministic dynamics were used as discriminating statistics. We analyze eight different stochastic and deterministic models not only to demonstrate the power of the surrogates, but also to reveal some pitfalls and limitations.

Type: Article
Title: Bivariate surrogate techniques: Necessity, strengths, and caveats
DOI: 10.1103/PhysRevE.68.066202
Keywords: TIME-SERIES DATA, PHASE SYNCHRONIZATION, GENERALIZED SYNCHRONIZATION, DYNAMICAL INTERDEPENDENCE, DETECTING NONLINEARITY, EEG, SYSTEMS, EPILEPSY, DIMENSION, SEIZURES
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Clinical and Movement Neurosciences
URI: http://discovery.ucl.ac.uk/id/eprint/113448
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