Background: The accuracy of source reconstruction depends on the spatial configuration of the neural sources underlying encephalographic signals, the temporal distance of the source activity, the level and structure of noise in the recordings, and – of course – on the employed inverse method. This plenitude of factors renders a definition of ‘spatial resolution’ of the electro-encephalogram (EEG) a challenge. New method: A proper definition of spatial resolution requires a ground truth. We used data from numerical simulations of two dipoles changed with waveforms resembling somatosensory evoked potentials peaking at 20, 30, 50, 100 ms. We varied inter-dipole distances and added noise to the simulated scalp recordings with distinct signal-to-noise ratios (SNRs). Prior to inverse modeling we pre-whitened the simulated data and the leadfield. We tested a two-dipole fit, sc-MUSIC, and sc-eLORETA and assessed their accuracy via the distance between the simulated and estimated sources. Results: To quantify the spatial resolution of EEG, we introduced the notion of separability, i.e. the separation of two dipolar sources with a certain inter-dipole distance. Our results indicate separability of two sources in the presence of realistic noise with SNR up to 3 if they are 11 mm or further apart. Comparison with existing methods: In the presence of realistic noise, spatial pre-whitening appears mandatory preprocessing step irrespective of the inverse method employed. Conclusions: Separability is a legitimate measure to quantify EEG's spatial resolution. An optimal resolution in source reconstruction requires spatial pre-whitening as a crucial pre-processing step.