The surface Laplacian transformation is a spatial transformation that does not alter the data domain, and consequently, CSD values can be treated just like surface potential values (i.e., using the same analytic tools). In fact, the combined used of surface Laplacian with other multivariate approaches, such as PCA (e.g., Kayser and Tenke, 2006a) or ICA (e.g., Fitzgibbon et al., 2014), has demonstrated considerable advantages. However, the challenge of how to identify and quantify appropriate dependent measures for the data at hand cannot be regarded as a valid reservation, because it equally applies to SP and SL data.
