The estimates of neural activity are usually obtained by either a localization approach or an imaging approach. The former assumes that brain activity at a given time point is generated by a limited number of neuronal pools, each represented by an equivalent current dipole (ECD), the location, orientation, and strength of which need to be estimated. Numerical approaches based on least-squares techniques are usually used to this purpose. The latter, i.e., the imaging approach, aims at estimating the overall distribution of neural activity by discretizing the brain, and thus is more suitable for whole brain functional connectivity estimation. Typically, a grid of elementary sources (dipoles), fixed in location and, possibly, in orientation, in the brain volume or limited to the cortical gray matter surface is used: the inverse problem solution for imaging approaches results in the estimation of the magnitude of all these elementary sources. Among imaging approaches, several strategies have been developed, including distributed source imaging methods (e.g., least-squares techniques such as minimum norm estimation approaches), scanning methods, and spatial filter methods. For reviews on the different imaging methods see Baillet et al. (2001); He et al. (2018); Ilmoniemi and Sarvas (2019).
