Functional Mixed-Effect Models for Electrophysiological Responses

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.