title: Comparison of different cortical connectivity estimators for high-resolution EEG recordings.
abstract: The aim of this work is to characterize quantitatively the performance of a body of techniques in the frequency domain for the estimation of cortical connectivity from high-resolution EEG recordings in different operative conditions commonly encountered in practice. Connectivity pattern estimators investigated are the Directed Transfer Function (DTF), its modification known as direct DTF (dDTF) and the Partial Directed Coherence (PDC). Predefined patterns of cortical connectivity were simulated and then retrieved by the application of the DTF, dDTF, and PDC methods. Signal-to-noise ratio (SNR) and length (LENGTH) of EEG epochs were studied as factors affecting the reconstruction of the imposed connectivity patterns. Reconstruction quality and error rate in estimated connectivity patterns were evaluated by means of some indexes of quality for the reconstructed connectivity pattern. The error functions were statistically analyzed with analysis of variance (ANOVA). The whole methodology was then applied to high-resolution EEG data recorded during the well-known Stroop paradigm. Simulations indicated that all three methods correctly estimated the simulated connectivity patterns under reasonable conditions. However, performance of the methods differed somewhat as a function of SNR and LENGTH factors. The methods were generally equivalent when applied to the Stroop data. In general, the amount of available EEG affected the accuracy of connectivity pattern estimations. Analysis of 27 s of nonconsecutive recordings with an SNR of 3 or more ensured that the connectivity pattern could be accurately recovered with an error below 7% for the PDC and 5% for the DTF. In conclusion, functional connectivity patterns of cortical activity can be effectively estimated under general conditions met in most EEG recordings by combining high-resolution EEG techniques, linear inverse estimation of the cortical activity, and frequency domain multivariate methods such as PDC, DTF, and dDTF.
authors.affiliation: Dipartimento Informatica e Sistemistica, Universita La Sapienza, Rome, Italy. laura.astolfi@uniromal.it
authors.forename: Laura
authors.initials: L
authors.lastname: Astolfi
authors.forename: Febo
authors.initials: F
authors.lastname: Cincotti
authors.forename: Donatella
authors.initials: D
authors.lastname: Mattia
authors.forename: M Grazia
authors.initials: MG
authors.lastname: Marciani
authors.forename: Luiz A
authors.initials: LA
authors.lastname: Baccala
authors.forename: Fabrizio
authors.initials: F
authors.lastname: de Vico Fallani
authors.forename: Serenella
authors.initials: S
authors.lastname: Salinari
authors.forename: Mauro
authors.initials: M
authors.lastname: Ursino
authors.forename: Melissa
authors.initials: M
authors.lastname: Zavaglia
authors.forename: Lei
authors.initials: L
authors.lastname: Ding
authors.forename: J Christopher
authors.initials: JC
authors.lastname: Edgar
authors.forename: Gregory A
authors.initials: GA
authors.lastname: Miller
authors.forename: Bin
authors.initials: B
authors.lastname: He
authors.forename: Fabio
authors.initials: F
authors.lastname: Babiloni
mesh_terms: D000704:Analysis of Variance; D001931:Brain Mapping; D002540:Cerebral Cortex / Q000502:physiology*; D003198:Computer Simulation; D004569:Electroencephalography; D006801:Humans; D008279:Magnetic Resonance Imaging; D008959:Models, Neurological; D009434:Neural Pathways / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted