To determine their common sources of variance, CSD waveforms were submitted to temporal PCA derived from the covariance matrix, followed by unrestricted Varimax rotation of the covariance loadings (Kayser & Tenke, 2003, 2006c). Using a MatLab function (appendix of Kayser & Tenke, 2003)2 that emulates BMDP-4M algorithms (Dixon, 1992), a temporal PCA was computed using 221 variables (time interval −100 to 1,000 ms) and 6,174 observations stemming from 42 participants, 3 conditions, and 49 electrode sites. A similar approach successfully determined the common sources of variance underlying the time-frequency CSD (cf. Tenke et al., 2012).3 Each 30-by-82 matrix was rearranged as a vector by concatenating the time vectors for each frequency, yielding a time-frequency vector of 2,460 ERD/ERS values. Using the same Matlab function (appendix of Kayser & Tenke, 2003), these data (i.e., 2,460 variables for 6,174 observations) were then submitted to unrestricted time-frequency PCA, using the covariance matrix for factorization and Varimax rotation of the covariance loadings.4
