in any of them (including the spatial precision of their location) may be exaggerated, while good (artifact-free) data can yield reliable CSD estimates via local methods. Further, the surface Laplacian for the peripheral electrodes cannot be estimated by these local methods (cf. Wang and Begleiter, 1999). On the other hand, global methods compute the surface Laplacian of the global potential function by using the potentials at all electrodes in interpolation functions, such as the spherical spline (Perrin et al., 1987a, 1989b). Realistic Laplacian estimator approaches assume that the best surface Laplacian estimates are computed by second order spline along with a smoothing parameter, and can be applied to any arbitrarily shaped scalp (Babiloni et al., 1995, 1996, 1998). The local polynomial approximations method proposed by Wang and Begleiter (1999) is: 1) a local method which assigns larger weight for closer electrodes; (2) able to estimate surface Laplacian at peripheral locations; (3) able to estimate potential and surface Laplacian simultaneously so that direct differentiation on an interpolated function is unnecessary; and (4) robust to noise. Compared to the second order spline estimation (Babiloni et al., 1995), the local polynomial approximations is better for cleaner data with high signal-to-noise-ratio (SNR), such as
