Imaging modalities such as EEG, MEG and fMRI are complimentary, making multi-modal data integration a necessary tool for a comprehensive understanding of brain function. For example, while EEG is sensitive to mostly shallow cortical sources, MEG has higher sensitivity to deep sources due to its higher spatial resolution. However, MEG has poor sensitivity to sources originating from clusters of neurons that are oriented radially to the skull. An integration of data originating from a simultaneous recording of EEG and MEG can hence reveal more information than one of these modalities alone. Developing computational models that are able to seamlessly integrate this information taking maximum advantage of their complementarity is therefore vital for this purpose. I am interested in pursuing these techniques as well as the integration of electophysiological data with fMRI data. We are currently exploring the integration of EEG and fMRI data acquired from a group of healthy controls and temporal lobe epilepsy patients.