Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Critical comments on EEG sensor space dynamical connectivity analysis

Van de Steen, Frederik, Faes, Luca, Karahan, Esin, Songsiri, Jitkomut, Valdes-Sosa, Pedro A. and Marinazzo, Daniele 2019. Critical comments on EEG sensor space dynamical connectivity analysis. Brain Topography 32 (4) , pp. 643-654. 10.1007/s10548-016-0538-7

Full text not available from this repository.

Abstract

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.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Psychology
Cardiff University Brain Research Imaging Centre (CUBRIC)
Publisher: Springer Verlag (Germany)
ISSN: 0896-0267
Date of Acceptance: 17 November 2016
Last Modified: 11 Mar 2023 02:26
URI: https://orca.cardiff.ac.uk/id/eprint/111003

Citation Data

Cited 75 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item