Inference of time-dependent causal influences in Networks
2012 (English)In: Biomedizinische Technik (Berlin. Zeitschrift), ISSN 0013-5585, Vol. 57Article in journal (Refereed) Published
We address the challenge of detecting time-variant interactions in multivariate systems. Inferring Granger-causal interactions between processes promises to gain deeper insights into mechanisms underlying network phenomena, e. g., in the neurosciences. Renormalized partial directed coherence (rPDC) has been introduced as a means to investigate Granger causality in such multivariate systems. When using rPDC a major challenge is the reliable estimation of parameters in vector autoregressive processes. For time-varying connections a time-resolved estimation of the coefficients is mandatory. We show that the State Space Model in combination with the Kalman filter is a powerful tool for estimating time-variate AR process parameters.
Place, publisher, year, edition, pages
Walter de Gruyter , 2012. Vol. 57
IdentifiersURN: urn:nbn:se:liu:diva-88374DOI: 10.1515/bmt-2012-4263ISI: 000312675100197OAI: oai:DiVA.org:liu-88374DiVA: diva2:602866