Comparison of different cortical connectivity estimators for high‐resolution EEG recordings

Abstract

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. Hum Brain Mapp 28:143-157, 2007.

Assessing cortico-hippocampal functional connectivity under anesthesia and kainic acid using generalized partial directed coherence

“…Although PDC has proved to be an accurate tool for the detection of direct connectivities, both in cases of coupled oscillators (Baccalá and Sameshima 2001;Winterhalder et al 2005;Gourévitch et al 2006;Schelter et al 2006a) or simple neuronal models of interconnected neurons (Sameshima and Baccalá 1999;Kamiński et al 2001;Astolfi et al 2007), it has been shown that large differences in the variances of the modeled time series can yield distortions in the resulting PDC values (Winterhalder et al 2005;Baccala et al 2007). For example, a set of three uncorrelated white noise processes, where two of them have much larger variance than the third, will produce a distorted connectivity profile, since PDC wrongly detects connections from the low-variance process to the other two (Winterhalder et al 2005).…”

The added value of the electrical neuroimaging for the evaluation of marketing stimuli

“…For each subject, the electrodes disposition on the scalp surface was generated through a nonlinear minimization procedure [12]. The cortical model consists of about 5,000 dipoles uniformly disposed on the cortical surface and the estimation of the current density strength for each dipole was obtained by solving the electromagnetic linear inverse problem according to the techniques described in previous papers [13][14][15]. For each experimental subject, the statistical significance of the Power Spectral Density (PSD) values elicited during the observation of the TV commercials was then measured against the activity evaluated during the observation of the documentary.…”

“…The system also includes the measurement noise n, assumed to be normally distributed [13][14][15][16].…”

The surface Laplacian technique in EEG: Theory and methods

“…These works were followed by efforts to develop better computational methods (see Gevins, 1988Gevins, , 1989Gevins et al, 1990;Perrin et al, 1989;Law et al, 1993a;Yao, 2002;Carvalhaes and Suppes, 2011) as well as attempts to combine the surface Laplacian with other methods (Kayser and Tenke, 2006b,a;Carvalhaes et al, 2009), making the technique increasingly popular among EEG researchers. For instance, modern applications include studies on generators of event-related potentials (Kayser and Tenke, 2006b,a), quantitative EEG (Tenke et al, 2011), spectral coherence , event-related synchronization/desynchronization (Del Percio et al, 2007), phase-lock synchronization (Doesburg et al, 2008), estimation of cortical connectivity (Astolfi et al, 2007), high-frequency EEG Fitzgibbon et al (2013), and brain-computer interface (Lu et al, 2013), just to mention a few.…”