The brain source localization problem has been extensively studied in the past years, yielding a large panel of methodologies, each bringing their own strengths and weaknesses. Combining several of these approaches might help in enhancing their respective performance. Our study is carried out in the particular context of intracranial recordings, with the objective to explain the measurements based on a reduced number of dipolar activities. We take benefit of the sparse nature of the Bayesian approaches to separate the noise from the source space, and to distinguish between several source contributions on the electrodes. This first step provides accurate estimates of the dipole projections, which can be used as an entry to an equivalent current dipole fitting procedure. We demonstrate on simulations that the localization results are significantly enhanced by this post-processing step when up to five dipoles are activated simultaneously.