Despite the recognition that the surface Laplacian may counteract adverse
effects of volume conduction and recording reference for surface potential data,
electrophysiology as a discipline has been reluctant to embrace this approach
for data analysis. The reasons for such hesitation are manifold but often
involve unfamiliarity with the nature of the underlying transformation, as well
as intimidation by a perceived mathematical complexity, and concerns of signal
loss, dense electrode array requirements, or susceptibility to noise. We revisit
the pitfalls arising from volume conduction and the mandated arbitrary choice of
EEG reference, describe the basic principle of the surface Laplacian transform
in an intuitive fashion, and exemplify the differences between common reference
schemes (nose, linked mastoids, average) and the surface Laplacian for
frequently-measured EEG spectra (theta, alpha) and standard event-related
potential (ERP) components, such as N1 or P3. We specifically review common
reservations against the universal use of the surface Laplacian, which can be
effectively addressed by employing spherical spline interpolations with an
appropriate selection of the spline flexibility parameter and regularization
constant. We argue from a pragmatic perspective that not only are these
reservations unfounded but that the continued predominant use of surface
potentials poses a considerable impediment on the progress of EEG and ERP
research.