It is easy to see how using a different EEG reference will lead to different ERP analyses and findings. A researcher may focus on the ‘obvious’ ERP deflections, be inclined to label them according to their polarity, peak latency and peak locations, measure certain characteristics (e.g., baseline-to-peak or integrated amplitude, peak latency), perform a statistical test, and refer to them as ERP components. However, as illustrated in Figure 2, this analytic strategy is misguided because the underlying neuronal generator activity has not been changed by rereferencing surface potentials. Even employing a multivariate data analytic approach will not resolve the ambiguity of when and where the ERP deflections are, because these approaches rely on the variance structure of the data, which is directly affected by the choice of reference. For example, principal components analysis (PCA) is often used for ERP analysis, with temporal, spatial or spatiotemporal PCA being fairly common (e.g., Barry and De Blasio, 2013; Kayser and Tenke, 2003; Spencer et al., 2001; van Boxtel, 1998). For a covariance association matrix, a common choice for factor extraction, the PCA identifies
