Electroencephalographic imaging is the estimation of 3D neuronal current sources on the cortical surface from the measured electroencephalogram (EEG). It is a highly under- determined inverse problem as there are many 'feasible' images which are consistent with the scalp potentials. Previous approaches to this problem have primarily concentrated on the weighted minimum norm inverse methods. While these methods ensure a unique solution, they often produce overly smoothed solutions and are sensitive to noise in the data. Our group previously proposed a maximum entropy approach to obtain better solutions to this problem. We incorporated a noise rejection term into the maximum entropy method, thereby making it analogous to a Bayesian maximum a posteriori formulation. Additional information from other modalities, like functional magnetic resonance imaging, could be incorporated into this method in the form of a prior bias function to improve solutions. While this approach gave better results than the minimum norm methods, the solutions were still somewhat smooth and blurry. In this work, we developed and tested an iterative version of the maximum entropy method to obtain more localized solutions. This method starts with a distributed estimate computed by the maximum entropy method. It then recursively performs maximum entropy estimations producing a progressively more focal current distribution. We present the method and test its validity through computer simulations for both noiseless and noisy data. The results suggest that the proposed method is a powerful algorithm with good utility for EEG imaging.
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