Pitfalls in EEG-based brain effective connectivity analysis

Stefan Haufe, Vadim V. Nikulin, Guido Nolte, Klaus Robert Müller

Research output: Contribution to journalConference articlepeer-review


We consider the problem of estimating brain effective connectivity from electroencephalographic (EEG) measurements, which is challenging due to instantaneous correlations in the sensor data caused by volume conduction in the head. We present selected results of a larger realistic simulation study in which we tested the ability of various measures of effective connectivity to recover the information flow between the underlying sources, as well as the ability of linear and nonlinear inverse source reconstruction approaches to improve the estimation. It turns out that factors related to volume conduction dramatically limit the neurophysiological interpretability of sensor-space connectivity maps and may even (depending on the connectivity measure used) lead to conflicting results. The success of connectivity estimation on inverse source estimates crucially depends on the correctness of the source demixing. This in turn depends on the capability of the method to model (multiple) interacting sources, which is in general not achievable by linear inverses.

Original languageEnglish
Pages (from-to)202-209
Number of pages8
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7263 LNAI
Publication statusPublished - 2012
Externally publishedYes
EventInternational Workshop on Machine Learning and Interpretation in Neuroimaging, MLINI 2011, Held at Neural Information Processing, NIPS 2011 - Sierra Nevada, Spain
Duration: 2011 Dec 162011 Dec 17


  • EEG
  • GC
  • PDC
  • PSI
  • S-FLEX
  • WMNE
  • brain effective connectivity
  • inverse source reconstruction
  • sensor space

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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