An eigenfunction approach to the inverse problem of EEG.

A mathematical method of extracting salient features from electroencephalographic data called eigenfunction analysis is presented. It allows the reduction of 21 channels of EEG data to a few components which can be separated into those which are likely to originate relatively close to the surface and others of deeper origin. It was demonstrated that the original tracings can be reconstituted from these few components. The eigenvectors given an indication of the location of sources and the degree to which the eigenfunction appears on source derivation and average reference recordings allows an estimation of relative depth. The method has been successfully applied to EEG tracings from 10 patients and is illustrated in the case of a young woman suffering from complex partial seizures associated with a deep left temporal lesion. The implications for marked data reduction and the development of objective assessment of clinical neurophysiologic data are discussed.