We have proposed that these limitations can be overcome without sacrificing information by combining current source density (CSD; surface Laplacian) and temporal principal components analysis (PCA) to identify relevant, data-driven components (Kayser & Tenke, 2006a, 2006b; Kayser et al., 2006, 2010; Kayser, Tenke, Gates,&Bruder, 2007; Kayser, Tenke, Gil, & Bruder, 2009; Tenke et al., 2008; Tenke, Kayser, Stewart, & Bruder, 2010). CSD provides a representation of current generators that underlie ERPs and represent the magnitude of radial current flow entering (sink) and leaving (source) the scalp (e.g., Nunez & Srinivasan, 2006). CSD analysis is a reference-free technique (any EEG recording reference scheme will yield the same, unique CSD transform) that provides sharper topographies compared to those of scalp potentials while also reducing redundant contributions due to volume conduction (e.g., Tenke & Kayser, 2005). Often-raised concerns include the requirement of a high-density EEG montage for reliably computing CSDs, as well as their presumed insensitivity to deep sources. We have empirically addressed these concerns, demonstrating that no information is lost with the CSD transform when directly compared to the original ERPs, and
