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Detecting information flow direction in multivariate linear and nonlinear models
Yang C., Le Bouquin-Jeannès R., Faucon G., Shu H.
Signal Processing 93, 1 (2013) 304-312 - http://www.hal.inserm.fr/inserm-00759802
Detecting information flow direction in multivariate linear and nonlinear models
Chunfeng Yang1, 2, Régine Le Bouquin-Jeannès () 1, 2, Gérard Faucon1, Huazhong Shu2, 3
1 :  LTSI - Laboratoire Traitement du Signal et de l'Image
INSERM : U1099 – Université de Rennes 1
Campus Universitaire de Beaulieu - Bât 22 - 35042 Rennes
2 :  CRIBS - Centre de Recherche en Information Biomédicale sino-français
INSERM : LABORATOIRE INTERNATIONAL ASSOCIÉ – Université de Rennes 1 – SouthEast University
3 :  LIST - Laboratory of Image Science and Technology [Nanjing]
SouthEast University – School of Computer Science and Engineering
Si Pai Lou 2, Nanjing, 210096
In this paper we present an approach to analyze the direction of information flow between time series involving bidirectional relations. The intuitive idea comes from a first study dedicated to the so-called phase slope index, which is a measure originally developed to detect unidirectional relations and is based on the complex coherence function. In order to detect bidirectional flows, we propose two new causality indices supplying the previous index with two other functions, the directed coherence function and the directed transfer function. Moreover, to cope with the inability of the approaches based on coherence (ordinary or directed) or on directed transfer function to distinguish between direct and indirect relations, we propose another causality index based on the partial directed coherence to identify only direct relations. Experimental results show that some challenges have promising solutions through the use of this new indicator dealing with both linear and nonlinear multivariate models.
Sciences du Vivant/Ingénierie biomédicale
Sciences de l'ingénieur/Traitement du signal et de l'image
Informatique/Traitement du signal et de l'image

Articles dans des revues avec comité de lecture
Signal Processing
Publisher Elsevier
ISSN 0165-1684 

Effective connectivity – Causal relations – Partial directed coherence – Information flow – Multivariate time series
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SP_Manuscript_Yang-review-SIGPRO-D-11-00830R3.pdf(200.4 KB)