This paper addresses the problem of sparse localization of cortical sources from scalp EEG recordings. Localization algorithms use propagation model under spatial and/or temporal constraints, but their performance highly depends on the data signal-to-noise ratio (SNR). In this work we propose a dictionary based sparse localization method which use data driven spatio-temporal dictionary to reconstruct the data using Single Best Replacement (SBR) and Continuation Single Best Replacement (CSBR) algorithm. We tested and compared our methods with well the known MUSIC and RAP-MUSIC algorithms on simulated realistic data. Tests were carried out for different noise levels. The results show that our method has a strong advantage among others in case of synchronized sources.