Critical Comments on EEG Sensor Space Dynamical Connectivity Analysis

  • Frederik Van de SteenEmail author
  • Luca Faes
  • Esin Karahan
  • Jitkomut Songsiri
  • Pedro A. Valdes-Sosa
  • Daniele Marinazzo
Original Paper


Many different analysis techniques have been developed and applied to EEG recordings that allow one to investigate how different brain areas interact. One particular class of methods, based on the linear parametric representation of multiple interacting time series, is widely used to study causal connectivity in the brain. However, the results obtained by these methods should be interpreted with great care. The goal of this paper is to show, both theoretically and using simulations, that results obtained by applying causal connectivity measures on the sensor (scalp) time series do not allow interpretation in terms of interacting brain sources. This is because (1) the channel locations cannot be seen as an approximation of a source’s anatomical location and (2) spurious connectivity can occur between sensors. Although many measures of causal connectivity derived from EEG sensor time series are affected by the latter, here we will focus on the well-known time domain index of Granger causality (GC) and on the frequency domain directed transfer function (DTF). Using the state-space framework and designing two simulation studies we show that mixing effects caused by volume conduction can lead to spurious connections, detected either by time domain GC or by DTF. Therefore, GC/DTF causal connectivity measures should be computed at the source level, or derived within analysis frameworks that model the effects of volume conduction. Since mixing effects can also occur in the source space, it is advised to combine source space analysis with connectivity measures that are robust to mixing.


EEG Brain connectivity MVAR Granger causality Directed transfer function 



This research was supported by the Fund for Scientific Research-Flanders (FWO-V), Grant FWO14/ASP/255.

Supplementary material

10548_2016_538_MOESM1_ESM.pdf (30 kb)
Supplementary material 1 (PDF 30 kb)
10548_2016_538_MOESM2_ESM.pdf (58 kb)
Supplementary material 2 (PDF 57 kb)


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Data AnalysisGhent UniversityGhentBelgium
  2. 2.Healthcare Research and Innovation Program FBK, Trento and BIOTech, Department of Industrial, EngineeringUniversity of TrentoMattarelloItaly
  3. 3.Key Laboratory for Neuroinformation of the Ministry of EducationUESTCChengduChina
  4. 4.Control Systems Laboratory, Electrical Engineering DepartmentChulalongkorn UniversityBangkokThailand
  5. 5.Cuban Neuroscience CenterLa HabanaCuba

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