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Free keywords:
Evoked potentials; False discovery rate; Mixed-effect analysis; Stochastic process; Wavelet
Abstract:
In electro/psychophysiological experiments, linear mixed-effect modeling is an effective statistical technique for data repeatedly observed from the same experimental participants or stimulus items. This review describes the application of mixed-effect modeling to functional responses, in particular those observed in event-related EEG or MEG experiments, using a discrete wavelet transform. The technique is illustrated with a design with several covariates, and procedures for generating posterior samples and computing a Bayesian false discovery rate are described. © 2009 Springer Science+Business Media, Inc.
