These limitations can be overcome by a generic analytic strategy that combines current source density (CSD; surface Laplacian) and temporal principal components analysis (PCA) to identify relevant, data-driven components (Kayser and Tenke, 2006a, 2006b; Kayser et al., 2006). First, reference-dependent surface potentials are transformed into reference-free CSD waveforms representing the radial current flow into (sources) and out of (sinks) the scalp (Tenke and Kayser, 2005). Due the elimination of redundant, volume-conducted contributions, any EEG reference will render the same, unique CSD waveforms for a given EEG montage. This will not only yield sharper topographies than ERPs, but will ultimately also improve the temporal resolution of the component structure. Second, unique and orthogonal variance patterns in these reference-free data are identified by unrestricted Varimax-PCA using the covariance matrix (Kayser and Tenke, 2003, 2005, 2006a, 2006b, 2006c). The preference for this CSD-PCA approach over traditional ERP analytic methods has been bolstered by sound empirical evidence demonstrating that experimental ERP effects were not compromised after eliminating ambiguities stemming from the recording reference, but were clarified and supplemented by reliable new insights (Kayser and
