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.

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The accuracy of EEG source localization depends on the choice of the inverse method, the resolution of the forward model, and the signal to noise ratio (SNR) of the recordings. Since we are interested in disentangling sources in proximity, the goal of our study is to examine the sensitivity of spatial resolution of EEG source reconstruction to a wide variety of factors like reconstruction method, SNR, orientation, inter-dipole distance and depth of the simulated dipoles, etc. We simulated time series to resemble waveforms of somatosensory evoked potentials. Inter-dipole distances and different dipole orientations were investigated as well as the effect of (realistic) noise. We employed both spherical and realistic head models. Source reconstruction was realized using a conventional stationary dipole model, MUSIC, self-consistent MUSIC (SC-MUSIC) algorithm, and e-LORETA. In addition to the above mentioned methods, a new approach is tested building upon the e-LORETA solution: the topography of the maximum of the e-LORETA distribution is projected out of the data before calculating the next e-LORETA inverse solution in a iterative process. The quality of fit (or localization) was defined as the distance between the simulated point- sources and either the estimated point-sources or the activity distributions by means of the Euclidean distance or of the Earth Mover’’s Distance, respectively. As expected, inter-dipole distances played an important role in the ability of every method to disentangle the simulated sources. Overall, SC-MUSIC appeared best suited for disentangling the two simulated sources even at high-noise simulations.@en