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 deep or distributed sources, such as P3, are adequately and sufficiently represented (Kayser & Tenke, 2006a). A low-density EEG montage may be as efficient as a dense electrode montage in summarizing CSD activity for group data, because group averages effectively impose a spatial low-pass filter to the data (Kayser & Tenke, 2006b). In the specific context of olfactory ERPs, for which generators are presumably deep (i.e., with origins in olfactory, gustatory, or limbic structures), the corresponding fields and CSDs will be more diffuse at scalp, rendering a low–resolution surface Laplacian an advantage, rather than a liability. Thus, these conventional concerns have been overstated, and CSDs have not only been proven to be useful but may constitute an optimal analytic approach for many practical ERP applications. Compared to more complex EEG source localization methods (Michel et al., 2004), relying on surface Laplacian estimates as an analytic strategy is more conservative because it completely avoids additional (and unproven) biophysical assumptions (tissue conductivity
