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Electroencephalography (EEG) and magneto-encephalography (MEG) are both currently used to reconstruct brain activity. The performance of inverse source reconstructions is dependent on the modality of signals in use as well as inverse techniques. Here we used a recently proposed sparse source imaging technique, i.e., the variation-based sparse cortical current density (VB-SCCD) algorithm to compare the use of EEG or MEG data in reconstructing extended cortical sources. We conducted Monte Carlo simulations as comparison to two other widely used source imaging techniques. The VB-SCCD technique was further evaluated in experimental EEG and MEG data. Our present results indicate that EEG and MEG have similar reconstruction performance as indicated by a metric, the area under the receiver operating characteristic curve (AUC). Furthermore, EEG and MEG have different advantages and limitations in revealing different aspects of features of extended cortical sources, which are complimentary to each other. A simultaneous EEG and MEG analysis framework is thus promising to produce much improved source reconstructions.