Both functional magnetic resonance imaging (fMRI)-constrained source analysis and independent component analysis (ICA) claim to estimate the neuronal sources of electroencephalographic (EEG) scalp signals. In fMRI-constrained source analysis, event-related potential (ERP) generator locations are defined by fMRI activation patterns, and their contribution to the scalp ERP signal is probed. In contrast, ICA assumes that networks of cortical generators can be separated on the basis of their statistical independence. While good arguments can be put forward to justify both approaches, it is unclear how results from both methods compare. A clarification of these issues is of utmost importance to reconcile findings made using identical paradigms but these two complementary analysis methods. As both methods share the concept of spatially static sources a natural space to compare both methods and to crossvalidate the respective findings is at the level of source activity in the form of dipole source waves and independent component time courses and their corresponding maps. We used fMRI-constrained source analysis and ICA followed by clustering using the Kuhn-Munkres algorithm to analyze data from a working memory experiment. We demonstrate that crossvalidation is indeed possible using an appropriate statistical test. However, the sensitivity of this crossvalidation approach is ultimately limited by the low number of dimensions that contribute significant variance to the grand average scalp ERP. We conclude that testing at the single-subject level is preferable for crossvalidation purposes if the signal-to-noise ratio of the data allows for this approach.