It is generally desirable to improve the signal-to-noise ratio of surface Laplacian estimates, which can be seriously compromised by EEG/ERP recording noise (e.g., Babiloni et al., 1995). To counteract this problem, a regularization parameter is used to smooth the interpolated surface potentials prior to the computation of the surface Laplacian (e.g., Nunez and Srinivasan, 2006). For the spherical spline surface Laplacian estimates, this smoothing constant has been termed lambda λ (Perrin et al., 1989). Comparing the effects of various spline orders (m = 2–5), montage densities (28 to 256 scalp sites), and spatial frequencies of noise (0.05 to 0.23 cycles/cm), Babiloni et al. (1995) found that spline flexibility strongly influenced the optimal choice for λ (i.e., greater spline flexibility required greater smoothing, with optimal values of 10−9 ≤ λ ≤10−2), indicating that λ correction also acts as a spatial filter. By comparison, spatial noise and montage density had only moderate impact for determining optimal values for λ. Importantly, λ correction significantly improved spherical spline surface Laplacian estimates when compared with the ‘analytic’ surface Laplacian distribution, which was directly computed from
