Identifying brain effective connectivity patterns from EEG: performance of Granger Causality, DTF, PDC and PSI on simulated data

Abstract

High temporal resolution and relatively low cost make electroencephalography (EEG) the most suitable nonin- vasive tool for studying brain dynamics. One of the major challenges is the determination of effective connectivity, i.e., directed (causal) information flow between brain areas. The common definition of effective connectivity is based on Granger’s argument, that “the cause must pre- cede the effect” [1], which is implemented by the original Granger Causality (GC) score [1], the Directed Transfer Function (DTF) [3], Partial Directed Coherence (PDC) [2] and the Phase-slope Index (PSI) [4], all of which have been applied to EEG data previously. However, due to volume conduction in the head, the original causally- related sources are mixed into EEG channels. We con- ducted a realistic simulation study to investigate how volume conduction affects EEG sensor-space effective connectivity estimation.