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Modern Electroencephalographic Assessment Techniques

Volume 91 of the series Neuromethods pp 171-204

Date:

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

  • Jlenia ToppiAffiliated withDepartment of Computer, Control, and Management Engineering Antonio Ruberti, Sapienza UniversityNeuroelectrical Imaging and BCI Lab, IRCCS Fondazione Santa Lucia Email author 
  • , Manuela PettiAffiliated withDepartment of Computer, Control, and Management Engineering Antonio Ruberti, Sapienza UniversityNeuroelectrical Imaging and BCI Lab, IRCCS Fondazione Santa Lucia
  • , Donatella MattiaAffiliated withNeuroelectrical Imaging and BCI Lab, IRCCS Fondazione Santa Lucia
  • , Fabio BabiloniAffiliated withDepartment of Physiology and Pharmacology, Sapienza University
  • , Laura AstolfiAffiliated withDepartment of Computer, Control, and Management Engineering Antonio Ruberti, Sapienza UniversityNeuroelectrical Imaging and BCI Lab, IRCCS Fondazione Santa Lucia

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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