Mass univariate analysis of event-related brain potentials/fields I: A critical tutorial review

By David M. Groppe, Thomas P. Urbach, Marta Kutas. Psychophysiology, 2011

1,163 citations recorded by the source corpus.

Event-related potentials (ERPs) and magnetic fields (ERFs) are typically analyzed via ANOVAs on mean activity in a priori windows. Advances in computing power and statistics have produced an alternative, mass univariate analyses consisting of thousands of statistical tests and powerful corrections for multiple comparisons. Such analyses are most useful when one has little a priori knowledge of effect locations or latencies, and for delineating effect boundaries. Mass univariate analyses complement and, at times, obviate traditional analyses. Here we review this approach as applied to ERP/ERF data and four methods for multiple comparison correction: strong control of the family-wise error rate (FWER) via permutation tests, weak control of FWER via cluster-based permutation tests, false discovery rate control, and control of the generalized FWER. We end with recommendations for their use and introduce free MATLAB software for their implementation.

Subfields: Cognitive Neuroscience, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/f447acefe38f48e7c61f0c4d0ebef66dd43cbc14

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Citations in the corpus

Committee report: publication guidelines and recommendations for studies using electroencephalography and magnetoencephalography. (2014, methodology): “The methods may involve calculation of permutation or bootstrapped distributions, without assuming that the data are uncorrelated or normally distributed (Groppe et al., 2011; Maris, 2012; Mensen & Khatami, 2013).”
How to Get Statistically Significant Effects in Any ERP Experiment (and Why You Shouldn’t) (2017, background): “A variety of correction approaches are now available, each with its own assumptions, strengths, and weaknesses (see Groppe et al., 2011a, for an excellent discussion).”
A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula (2016, methodology): “To apply MI in this paradigm, each poststimulus time point r G aussian C opula M utual I nformation r r 1547 r and sensor/source is treated independently within a mass-univariate framework [Groppe et al., 2011].”
Distractor anticipation during working memory is associated with theta and beta oscillations across spatial scales (2024, unknown): “To test the strength of functional connectivity with respect to the LPFC, we estimated coherence between the dimensionally-reduced LFP from the LPFC and EEG at each included EEG electrode channel, using the cross-spectral density normalized by the product of the two auto-spectral densities.”