There are many methods for computing the surface Laplacian of brain potentials, which include: 1) local methods (e.g., Hjorth, 1975), 2) global methods (e.g., Perrin et al., 1987a), 3) ‘realistic geometry’ or ‘realistic Laplacian estimator’ (Babiloni et al., 1996), and 4) local polynomial approximations (Wang and Begleiter, 1999). Each of these methods has its own strengths and weaknesses. Some local methods compute the surface Laplacian by using only the potentials at "nearest neighbor" electrodes in the algorithm, such as a finite difference scheme (Hjorth, 1975; Katznelson, 1981) and a least squares solution of fitting a local quadratic representation of the potentials (Le et al., 1994). However, the surface Laplacian of brain potentials computed by local methods are said to be very sensitive to noise, with amplified high spatial frequencies (Le et al., 1994). Given a small number of electrodes, noise 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
