An extension of this nearest-neighbor algorithm from one-dimensional data arrays to two-dimensional surfaces is the local Hjorth (Hjorth, 1975, 1980). Here, an estimate of the second spatial derivative is likewise computed by subtracting the potentials of all neighboring sites weighted by their inverse distance from the potential measured at a given location. This requires the designation of a differentiation grid for a given EEG montage (i.e., the number and location of neighbors for each recording site), with number of nearest neighbors typically varying between 3 and 5 (cf. also Eq. 1 and Fig. 1 in Tenke et al., 1998), although any number of ‘nearest’ neighbors (up to n-1recording sites) can be defined. Figure 5 shows a 3–5 nearest neighbor local Hjorth grid for the 67-channel EEG montage shown in Figure 2. In this case, for example, the surface Laplacian at site Cz is estimated from the surface potentials measured at Cz and its four nearest neighbors (C1, C2, FCz, CPz). Given that these neighbors have all the same distance d to the Cz, owing to the fact that these are
