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

Authors


  • The authors would like to thank Ryan Canolty, Sandy Clarke, Ed Korn, and Bradley Voytek for help with researching this article. This research was supported by US National Institute of Child Health and Human Development grant HD22614 and National Institute of Aging grant AG08313 to Marta Kutas and a University of California, San Diego Faculty Fellows fellowship to David Groppe. All data used for the research reported in this manuscript were collected from human volunteers who were 18 years of age or older and participated in the experiments for class credit or pay after providing informed consent. The University of California, San Diego Institutional Review Board approved the experimental protocol.

Address correspondence to: David M. Groppe, Department of Cognitive Science, 0515 University of California, San Diego, 9500 Gilman Dr., La Jolla, CA 92093-0515. E-mail: dgroppe@cogsci.ucsd.edu

Abstract

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.

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