A factor-adjusted multiple testing procedure for ERP data analysis

Event-related potentials (ERPs) are now widely collected in psychological research to determine the time courses of mental events. When event-related potentials from treatment conditions are compared, often there is no a priori information on when or how long the differences should occur. Testing simultaneously for differences over the entire set of time points creates a serious multiple comparison problem in which the probability of false positive errors must be controlled, while maintaining reasonable power for correct detection. In this work, we extend the factor-adjusted multiple testing procedure developed by Friguet, Kloareg, and Causeur (Journal of the American Statistical Association, 104, 1406-1415, 2009) to manage the multiplicity problem in ERP data analysis and compare its performance with that of the Benjamini and Hochberg (Journal of the Royal Statistical Society B, 57, 289-300, 1995) false discovery rate procedure, using simulations. The proposed procedure outperformed the latter in detecting more truly significant time points, in addition to reducing the variability of the false discovery rate, suggesting that corrections for mass multiple testings of ERPs can be much improved by modeling the strong local temporal dependencies.