Fast communication: Detecting information flow direction in multivariate linear and nonlinear models

  • Authors:

  • Chunfeng Yang;RéGine Le Bouquin JeannèS;GéRard Faucon;Huazhong Shu

  • Affiliations:

  • U1099, INSERM, F-35000 Rennes, France and LTSI, Université de Rennes 1, F-35000 Rennes, France and LIST, School of Computer Science and Engineering, Southeast University, 210096 Nanjing, Chin ...;U1099, INSERM, F-35000 Rennes, France and LTSI, Université de Rennes 1, F-35000 Rennes, France and Centre de Recherche en Information Biomédicale Sino-Français (CRIBs);U1099, INSERM, F-35000 Rennes, France and LTSI, Université de Rennes 1, F-35000 Rennes, France and Centre de Recherche en Information Biomédicale Sino-Français (CRIBs);LIST, School of Computer Science and Engineering, Southeast University, 210096 Nanjing, China and Centre de Recherche en Information Biomédicale Sino-Français (CRIBs)

  • Venue:
  • Signal Processing
  • Year:
  • 2013

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Abstract

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.