Decision algorithms are developed that use periods of intracranial non-seizure (interictal) EEG to localize epileptogenic networks. Depth and surface recordings are considered from 5 and 6 patients respectively. The proposed algorithms combine spectral and multivariate statistics in a decision-theoretic framework to automatically delineate the seizure onset area. In the case of depth recordings, we apply standard binary classification algorithms, including linear and quadratic discriminative analysis. For the surface recordings, novel decision algorithms are developed, based upon graphical models. The outcomes from the algorithms for both depth and surface recordings are in good agreement with the determination of the seizure focus by clinicians from ictal EEG. In the long term, the proposed approach may lead to shorter hospitalization of intractable-epilepsy patients, since it does not rely on ictal EEG.