A critical assessment of connectivity measures for EEG data: A simulation study

By Stefan Haufe, Vadim V. Nikulin, K. Müller, Guido Nolte. NeuroImage, 2013

355 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/4f099b68a2e255e49a7d6a648f703173de1f1313

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Citations in the corpus

Real-time Neuroimaging and Cognitive Monitoring Using Wearable Dry EEG (2015, neutral): “lar, measuring interactions at the level of cortical sources, rather than sensors can offer increased interpretability, while reducing confounding factors of volume conduction [12]–[14].”
EEG and MEG: relevance to neuroscience. (2013, background): “A variety of methods of signal analysis to estimate functional connectivity exist (Lachaux et al., 1999; Stam et al., 2007; Wendling et al., 2009; Vinck et al., 2011; Haufe et al., 2013), among which the phase-locking value (PLV) is currently the most widely used.”
On the interpretation of weight vectors of linear models in multivariate neuroimaging (2014, background): “Generally, weencourage authors to test sophisticated methods in realistic simulations to better understand their properties before applying them to real data, a point also made by Haufe et al. (2012) in the context of causal estimation.”
Phase transfer entropy: A novel phase-based measure for directed connectivity in networks coupled by oscillatory interactions (2014, background): “Signal mixing can result in the detection of artificial AC C EP TE D M AN U SC R IP T connectivity between nearby sources (Haufe et al., 2012; Palva and Palva, 2012) and always mirrors true interactions into spurious connectivity among adjacent regions (Palva and Palva, 2012).”
HERMES: Towards an Integrated Toolbox to Characterize Functional and Effective Brain Connectivity (2013, methodology): “…method of twin surrogates (Thiel et al. 2006), which is included in the Cross Recurrence Plot toolbox (Marwan et al. 2007) and the recently derived time reversed surrogates, which are very useful in combination with GC measures, to discard indirect connections in sensor space (Haufe et al. 2013).”
MNE software for processing MEG and EEG data (2013, background): “We refer to Haufe et al. (2012) for a recent simulation study that analyzes this issue.”
Quilting the Brain: Whole-Brain iEEG Reconstruction via Incomplete Observation Linear Mixed Models (2026, unknown): “…where the skull and scalp act as low-pass (cid:28)lters, resulting in the loss of high-frequency signals and blurred source localization (Haufe et al., 2013; Van de Steen et al., 2019; Gonzalez-Moreira et al., 2020); conversely, fMRI relies on the hemodynamic response, the temporal…”
Cortical-limbic circuit dynamics of approach-avoidance conflict in humans (2026, unknown): “We next subtracted the spectral Granger values calculated from the reversed time-series, as noise can in ﬂ ate connectivity estimates if the signal-to-noise-ratio SNR increases or decreases over time 40 .”