Optimal Methods for ERP/MRI Source Estimation

This project will develop an optimal methodology for non-invasive estimation and localization ofreal-time neural activity generated within multiple intracranial sources. This methodology will integrateEEG/ERP inverse methods with anatomical information from magnetic resonance imaging (MRI) toconstruct optimal head models and to constrain possible source geometries. The commercial aim is todevelop modular software that supports a variety of source and head models for the analysis ofsurface-recorded electrophysiologic data. Immediate applications are envisioned in basic and clinicalresearch ranging from functional neuroanatomy to cognitive psychophysiology. This user research willlikely produce primary clinical applications in neurosurgery, neurology, psychiatry and neuropsychology.Phase I consists of a volume conductor study followed by an inverse operator study. Two new methodsare derived for fast computation of volume conduction transfer coefficients using head geometry data:source-to-scalp geometric fitting, and transform to the unbounded volume. Transfer coefficient errorswill be measured using the boundary element method as a standard. Applying different methods tosimulated data constructed from actual MRI and EEG/ERP data, the inverse operator study will revealthe effects of measurement errors produced by coherently colored background EEG, and interactionswith approximation errors produced by simplified head models.