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Mass univariate analysis of event‐related brain potentials/fields I: A critical tutorial review
Author(s) -
Groppe David M.,
Urbach Thomas P.,
Kutas Marta
Publication year - 2011
Publication title -
psychophysiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.661
H-Index - 156
eISSN - 1469-8986
pISSN - 0048-5772
DOI - 10.1111/j.1469-8986.2011.01273.x
Subject(s) - univariate , permutation (music) , a priori and a posteriori , multiple comparisons problem , false discovery rate , complement (music) , statistics , computer science , variance (accounting) , event (particle physics) , multivariate analysis of variance , control (management) , psychology , data mining , multivariate statistics , artificial intelligence , mathematics , philosophy , physics , biochemistry , chemistry , accounting , epistemology , quantum mechanics , complementation , acoustics , business , gene , phenotype
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 familywise 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.