The highest temporal resolution, which is crucial for temporal localization of intracerebral activities, is achieved by ERP, but spatial resolution of scalp topography is low. To overcome the limitation of scalp topography, several current-density estimation techniques were developed whose goal is to find the locations of the three-dimensional (3D) intracerebral activities by solving an inverse problem (such as LORETA). However, scalp topologies constituted by multiple sources which makes the inverse problem complicated. The overall objective of this work is to isolate spatial frequency components of scalp topography by 2-D wavelet transform and to interpret spatial frequency formation via corresponding current-density estimations. Moreover, by achieving less complex scalp maps, obstacle of the inverse problem due to the multiple sources might be lessen. At the first step, main topologies of ERP recordings were investigated by hierarchical clustering algorithm. Secondly, different spatial frequencies of these main topologies were separated by 2-D wavelet transform. Finally, main topological maps and topographic maps of different spatial frequencies derived from them were used to find corresponding cortical activities by LORETA. Assessment of our spatial analysis results was made according to the current density estimation results.