High-resolution EEG technologies have been developed to enhance the poor spatial information content of the EEG activity. 

These technologies consist essentially of high spatial sampling (with 64-128 channels) and surface Laplacian (SL) (Nunez et al., 1994) or spatial de-convolution (SD) estimations (Le and Gevins, 1993). The estimation of the SL of the potential needs the modeling of the scalp surface, while the SD estimation is based on the construction of a multi-compartment head volume conductor for simulating cortex, dura mater, skull and scalp surfaces. Most recently, the developed high-resolution EEG enhancement technologies use realistic MRI-constructed subject’s head models (Babiloni et al., 1997). SL is computed by a spline Laplacian estimator, and SD by a linear inverse estimation method based on boundary-element (BEM) mathematics. 

The accuracy of the neuroelectrical inverse problem solution space can also benefit from a priori information coming from other modalities. The mathematical framework is well suited to include information deriving from hemodynamic measures (i.e., functional MRI BOLD phenomenon) recorded in comparable experimental conditions (Babiloni et al. 2005b).

The key-point of high-resolution EEG technologies is the availability of an accurate model of the head as a volume conductor to be used with advanced computational techniques such as SL or SD. However, appropriate techniques have to be used in order to register the electrode positions on the scalp model. 

Several authors have shown that it is possible to improve the spatial resolution of EEG by using sophisticated computational algorithms and detailed geometrical models of the head as a volume conductor with the help of the MRI data (Babiloni et al., 2000a,; Gevins et al., 1994; Nunez, 1995).

Next figure shows the steps for the estimation of the cortical activity from the EEG potential on the scalp