A self-coherence enhancement algorithm and its application to enhancing three-dimensional source estimation from EEGs

In this paper a new algorithm is proposed to enhance the spatial resolution of solutions of the underdetermined EEG inverse problem. Termed the self-coherence enhancement algorithm (SCEA), the present algorithm provides a self-coherence solution, which is a function of the high order self-coherence estimate of an unbiased smooth estimate of the underdetermined EEG inverse solution. The order of the high order self-coherence function is determined by the blurring level of the actual source distribution as represented by a normalized blurring index. The proposed SCEA algorithm may be used to enhance the spatial resolution of an inverse solution obtained by any inverse reconstruction algorithm. Computer simulation studies have been conducted to evaluate the performance of the SCEA and to compare its performance to that of the LORETA and the FOCUSS algorithms.