To interpret the resulting factors, their loading vectors were restored to 30-by-82 matrices to create conventional time-frequency plots. This approach, which is akin to the joint (simultaneous) extraction of stimulus- and response-locked components in the time domain (cf. Kayser et al., 2007), was preferred to a multiple-stage simplification (i.e., time and frequency) because of its simplicity and ease of interpretation, recognizing the ability of PCA to identify spectral and/or temporal processes that belong together. Similarly, noting that the specific sequence or order of variables is irrelevant for the PCA decomposition (e.g., Kayser & Tenke, 2003), Bernat et al. (2005) applied this strategy to time-frequency matrices, likewise producing factors from the vectorized matrices and, after factor restriction (Scree criterion), converting the rotated factor loadings vectors back to time-frequency matrices for easy interpretation. However, their study employed field potentials for a comparison of spectra derived from wavelets or reduced interference distributions, in contrasts to the present FFT-based ERSPs derived from CSDs. By virtue of the reference-independent Laplacian transform (see Tenke & Kayser, 2012, for a review), both the temporal (tPCA) and the
