Spatio-temporal multiple source localization by wavelet-type decomposition of evoked potentials

Scalp recording of electrical events allows evaluation of human cerebral function, but contributions of the specific brain structures generating the recorded activity are ambiguous. This problem is ill-posed and cannot be solved without auxiliary physiological knowledge about the spatio-temporal characteristics of the generators' activity. In our source localization by model-based wavelet-type decomposition, scalp recorded signals are decomposed into a combination of wavelets, each of which may describe the coherent activity of a population of neurons. We chose the Hermite functions (derived from the Gaussian function to form mono-, bi- and triphasic wave forms) as the mathematical model to describe the temporal pattern of mass neural activity. For each wavelet we solve the inverse problem for two symmetrically positioned and oriented dipoles, one of which attains zero magnitude when a single source is more suitable. We use the wavelet to model the temporal activity pattern of the symmetrical dipoles. By this we reduce the dimension of inverse problem and find a plausible solution. Once the number and the initial parameters of the sources are given, we can apply multiple source localization to correct the solution for generators with overlapping activities. Application of the procedure to subcortical and cortical components of somatosensory evoked potentials demonstrates its feasibility.