Event-related potentials recorded from the human scalp using EEG can provide important information about how the human brain processes information. To unravel cognitive processes, the so-called lateralized readiness potential (LRP) has become an especially useful temporal marker within the area of chrono-psychophysiology. Simple parametric modeling of such LRP-curves is unsufficient and nonparametric approaches allowing for arbitrary functional forms are warranted. Smoothing methods using global bandwidths and penalties are often used to model such longitudinal data with curves but fail to capture spatial heterogeneity and local features like peaks. Using kernels with local bandwidths and adaptive penalties can address these issues in the single-function setting, but are not easily generalized to the multiple-function setting. Wavelet-based functional mixed models (WFMM) as proposed by Morris and Carroll (JRSS B, 2006, Vol.68, pp179-199) may offer a valuable alternative in this setting. We aim to illustrate WFMM to LRP-data from a task switching study. More specifically we are interested to determine the timepoint/latency at which a consistent divergence in LRP-signals between task repetitions and task switches can be observed and to investigate whether this latency is influenced by other factors (such as cuing type, indication requirement, ...). Repeated testing at different timepoints induces a multiple testing problem (MTP). We compare the performance of different multiple testing procedures using the pointwise posterior credible intervals versus inference from the joint posterior credible interval
