Time-variant partial directed coherence for analysing connectivity: A methodological study

L. Leistritz, B. Pester, A. Doering, K. Schiecke, F. Babiloni, L. Astolfi, H. Witte

Research output: Contribution to journalArticlepeer-review


For the past decade, the detection and quantification of interactions within and between physiological networks has become a priority-in-common between the fields of biomedicine and computer science. Prominent examples are the interaction analysis of brain networks and of the cardiovascular-respiratory system. The aim of the study is to show how and to what extent results from time-variant partial directed coherence analysis are influenced by some basic estimator and data parameters. The impacts of the Kalman filter settings, the order of the autoregressive (AR) model, signal-to-noise ratios, filter procedures and volume conduction were investigated. These systematic investigations are based on data derived from simulated connectivity networks and were performed using a Kalman filter approach for the estimation of the timevariant multivariate AR model. Additionally, the influence of electrooculogram artefact rejection on the significance and dynamics of interactions in 29 channel electroencephalography recordings, derived from a photic driving experiment, is demonstrated. For artefact rejection, independent component analysis was used. The study provides rules to correctly apply particular methods that will aid users to achieve more reliable interpretations of the results.

Original languageEnglish
Article number20110616
JournalPhilosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
Issue number1997
Publication statusPublished - Aug 28 2013


  • Effective connectivity
  • Electrooculogram artefact rejection
  • Independent component analysis
  • Partial directed coherence
  • Time-variant multivariate autoregressive modelling

ASJC Scopus subject areas

  • Mathematics(all)
  • Physics and Astronomy(all)
  • Engineering(all)


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