Inference of time-dependent causal influences in Networks

M. Killmann, L. Sommerlade, W. Mader, J. Timmer, B. Schelter
2012 Biomedical Engineering  
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
more » ... es. 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.
doi:10.1515/bmt-2012-4263 fatcat:usl447vourc6poac2l24v26x2a