Finally, the linear multivariate data-reduction approach for CSD-transformed surface potentials using unrestricted, covariance-based Varimax-PCA (e.g., Kayser & Tenke, 2003), which we have successfully applied to novelty oddball ERPs (CSD-tPCA; Tenke et al., 2010) or EEG spectra at rest (CSD-fPCA; e.g., Tenke & Kayser, 2005), has also been useful for the systematic decomposition of reference-free ERSP data (CSD-tfPCA; Tenke et al., 2012). As a logical extension of our previous CSD-PCA methods, this data-driven approach identified and effectively summarized meaningful EEG oscillations within the theta and alpha spectrum without being constrained by rigid spectral (e.g., classic frequency bands) or temporal boundaries (i.e., static time windows). As efficient summaries of the database, the neuronal generator patterns underlying these event-related oscillations can be readily depicted as topographies, thereby providing details about regional contributions to ERS or ERD not seen with conventional time-frequency EEG analysis. For example, this approach revealed scalp locations of maximum alpha/theta energy that could be directly related to CSD dipoles observed in the time domain (e.g., N1 ERS with N1 sink, NVS ERS with NVS), or be plausibly linked to known
