EEC source localization: A neural network approach

Functional activity in the brain is associated with the generation of curre nts and resultant voltages which may be observed on the scalp as the electr oencephelogram. The current sources may be modeled as dipoles. The properti es of the current dipole sources may be studied by solving either the forwa rd or in verse problems. The forward problem utilizes a volume conductor mo del for the head, in which the potentials on the conductor surface are comp uted based on an assumed current dipole at an arbitrary location, orientati on, and strength. In the inverse problem, on the other hand, a current dipo le, or a group of dipoles, is identified based on the observed EEG. Both th e forward and inverse problems are typically solved by numerical procedures , such as a boundary element method and an optimization algorithm. These ap proaches are highly time-consuming and unsuitable for the rapid evaluation of brain function. in this paper we present a different approach to these p roblems based on machine learning. We solve both problems using artificial neural networks which are trained off-line using back-propagation technique s to learn the complex source-potential relationships of head volume conduc tion. Once trained, these networks are able to generalize their knowledge t o localize functional activity within the brain in a computationally effici ent manner.