Modern Electroencephalographic Assessment Techniques pp 171-204

Part of the Neuromethods book series (NM, volume 91) | Cite as

Time-Varying Effective Connectivity for Investigating the Neurophysiological Basis of Cognitive Processes

  • Jlenia Toppi
  • Manuela Petti
  • Donatella Mattia
  • Fabio Babiloni
  • Laura Astolfi
Protocol

Abstract

This chapter describes the methodological advancements developed during the last 20 years in the field of effective connectivity based on Granger causality and linear autoregressive modeling. At first we introduce the concept of Granger causality and its application to the connectivity field. Then, a detailed description of both stationary and time-varying versions of Partial Directed Coherence (PDC) estimator for effective connectivity will be given. The General Linear Kalman Filter (GLKF) approach is described an algorithm, recently introduced for estimating the temporal evolution of the parameters of adaptive multivariate model, able to overcome the limits of existing time-varying approaches. Then a detailed description of the graph theory approach and of possible indexes which could be defined is given. At the end, the potentiality of the described methodologies is demonstrated in an application aiming at investigating the neurophysiological basis of motor imagery processes.

Keywords

Electroencephalography (EEG) Effective connectivity Non-stationarity Graph theory Statistical assessment Multiple comparisons Motor imagery 

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Jlenia Toppi
    • 1
    • 2
  • Manuela Petti
    • 1
    • 2
  • Donatella Mattia
    • 2
  • Fabio Babiloni
    • 3
  • Laura Astolfi
    • 1
    • 2
  1. 1.Department of Computer, Control, and Management Engineering Antonio RubertiSapienza UniversityRomeItaly
  2. 2.Neuroelectrical Imaging and BCI LabIRCCS Fondazione Santa LuciaRomeItaly
  3. 3.Department of Physiology and PharmacologySapienza UniversityRomeItaly

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