As is common when using temporal PCA as a multivariate, linear data decomposition approach for ERP analysis, there is no need to back-project the extracted factors into the original data space (in this case, µV/cm2) because the associated factor scores already provide optimal quantifications of the factors (e.g., Chapman & McCrary, 1995; Donchin & Heffley, 1978; Kayser & Tenke, 2003; van Boxtel, 1998). In case of a covariance-based temporal PCA, the factor scores can be considered as weighted time window integrals (i.e., amplitudes) for each factor, with the additional statistical benefit of having a mean of zero (across all cases) and a standard deviation of one (Kayser & Tenke, 2003). Thus, it may be not surprising that these factors describe the variance contributions of temporally and spatially overlapping ERP or CSD components more efficiently than conventional measures, such as baseline-to-peak or integrated time windows, yielding larger effect sizes and higher reliabilities (e.g., Beauducel et al., 2000; Beauducel & Debener, 2003; Kayser et al., 1997, 1998).
