The main objective of this tutorial review is to introduce the use of SL to EEG researchers unfamiliar with or currently skeptical of this approach, and to argue for the routine use of SL methods in EEG research from a pragmatic perspective. For this purpose, we first visually illustrate the EEG reference problem and its impact on event-related potential (ERP) data analysis. Second, we show how the SL transformation works in principle, building on simple one-dimensional and two-dimensional (local Hjorth) Laplacian implementations to offer an intuitive description of SL computation via spherical splines, along with general considerations for practical use in EEG studies. Third, we provide a side-by-side comparison of typical EEG and ERP measures using reference-dependent surface potentials and their SL counterparts, including alpha and theta power, auditory P3(00), visual N1, mismatch negativity (MMN), and ERN-like activations (components). Finally, we revisit and directly address previous reservations regarding the use of the SL, including concerns that SL measures represent activations at a different spatial scale than surface potentials. However, it is beyond the scope of this paper to detail the
