The principal goal here is to improve the use of electromagnetic source imaging (ESI is the combined use of MEG, EEG and MRI) in clinical and research practice through the development of better algorithms for image reconstruction and analysis. Another goal is the development of tools for integration of ESI with other imaging modalities such as DTI and fMRI. Ongoing projects include the development of various algorithms for selective signal cancellation, separation and localization of brain sources from EEG and MEG data. We draw upon advances in statistical signal processing, machine learning, and probabilistic Bayesian inference and apply such techniques to analyze MEG and EEG data. Another ongoing effort is to develop algorithms for automated detection and localization of epileptogenic zones. All Algorithms are validated by comparing ESI reconstructions with electrocorticography (ECoG) recordings in patients with brain tumors and in patients with epilepsy. Algorithm validation is also performed in animal models by comparing imaging data such as ESI reconstructions and DTI connectivity with electrophysiological and neuroanatomical measurements. Clinical applications of this work will be tested in patients with brain tumors and in patients with epilepsy.