Classification of biomagnetic field patterns by neural networks

When analyzing biomagnetic fields, three-dimensional source reconstruction plays an important role. A method is presented which facilitates preprocessing for such a reconstruction. A neural net classifier decides whether from a given magnetoencephalographic map a localization using a given source model can be carried through. The performance in practical applications is compared for various types of networks. Some new information about the properties of the biomagnetic inverse problem is obtained by extracting rules from the trained nets.