Reference-dependent surface potentials are ambiguous in all key aspects of the EEG signal (polarity, topography, latency). Common approaches to identify and quantify the important characteristics of these data will lead to different results, with the extent of these differences ranging from minor discrepancies to diametrically opposite findings. The surface Laplacian is a unique, linear data transformation that maintains the invariant (i.e., reference-independent) aspects of the EEG signal, thereby resolving all of these ambiguities. As reviewed here, spherical spline interpolation provides a convenient means to obtain continuous estimates of radial current flow at scalp for low- and high-density EEG montages. The CSD distributions represent neuronal generator patterns in space and time that can (and should) be analyzed via the same analytic approaches already employed for surface potentials. Appropriate selection of spline interpolation parameters can counteract known limitations of the surface Laplacian (i.e., spatial high pass) without resorting to the notorious pitfalls of surface potentials. However, more work is still required to study the applicability of SL estimates based on less flexible splines in the context of functional connectivity and coherence for
