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Linear and nonlinear causality between signals: methods, examples and neurophysiological applications

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Abstract

In this paper, we will present and review the most usual methods to detect linear and nonlinear causality between signals: linear Granger causality test (Geweke in J Am Stat Assoc 77:304–313, 1982) extended to direct causality in multivariate case (LGC), directed coherence (DCOH, Saito and Harashima in Recent advances in EEG and EMG data processing, Elsevier, Amsterdam, 1981), partial directed coherence (PDC, Sameshima and Baccala 1999) and nonlinear Granger causality test of Baek and Brock (in Working Paper University of Iowa, 1992) extended to direct causality in multivariate case (partial nonlinear Granger causality, PNGC). All these methods are tested and compared on several ARX, Poisson and nonlinear models, and on neurophysiological data (depth EEG). The results show that LGC, DCOH and PDC are not very robust in relation to nonlinear linkages but they seem to correctly find linear linkages if only the autoregressive parts are nonlinear. PNGC is extremely dependent on the choice of parameters. Moreover, LGC and PNGC may give misleading results in the case of causality on a spectral band, which is illustrated by our neurophysiological database.

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  1. Laboratoire Traitement du Signal et de l’Image, Inserm U642, Université de Rennes 1, Campus de Beaulieu, Bât. 22, 35042, Rennes Cedex, France

    Boris Gourévitch, Régine Le Bouquin-Jeannès & Gérard Faucon

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  1. Boris Gourévitch
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  2. Régine Le Bouquin-Jeannès
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Correspondence to Boris Gourévitch.

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Gourévitch, B., Bouquin-Jeannès, R.L. & Faucon, G. Linear and nonlinear causality between signals: methods, examples and neurophysiological applications. Biol Cybern 95, 349–369 (2006). https://doi.org/10.1007/s00422-006-0098-0

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