Related problems concern: 1) how to best quantify ERP effects (i.e., group and/or condition differences) in multichannel surface potentials with or without “obvious” and “meaningful” waveform peaks, some of which may easily go unnoticed in grand mean averages given the overwhelming temporal and spatial complexity; 2) how to avoid experimenter bias is selecting time intervals and recording sites for statistical analysis; and 3) how to ensure statistical independency of the analyzed effects (cf. Kayser and Tenke, 2003, 2005). All studies reviewed in the previous section differ in this regard, for instance, ranging from baseline-to-peak amplitude measures within certain time intervals (Baving et al., 2000), including individual peak identification used to determine individual time windows for amplitude integrals (Guillem et al., 2001) and fixed, sequential time window amplitudes at a priori determined sites covering regions-of-interest (Tendolkar et al., 2002), to data-driven ERP components measures derived from temporal principal components analysis (PCA; Kayser et al., 1999). It is not unreasonable to suspect that these different analytic approaches may themselves have led to different results and conclusions when applied to the same data.
