Simulations were performed using SPM8 capabilities. Traditional EEG approach was implemented using EEGlab toolbox; fMRI approach was applied to sLORETA-transformed EEG data using GIFT toolbox. It was tested how well the two approaches are able to distinguish two simulated EEG sources that vary in their localization, frequency, and within- and between-subject variation.
With two widely spaced sources oscillating at different frequencies, both approaches were similarly effective. With two closely spaced sources oscillating at different frequencies, the EEGlab method performed somewhat better. However, it failed to distinguish two widely spaced sources if they oscillated at the same frequency and had the same temporal dynamics.
The proposed approach is feasible to apply to EEG data, particularly in a study of temporally correlated processes occurring within the same frequency band.
The proposed approach is straightforward for comparison of EEG results with the existing fMRI framework and for investigation of neural networks in normal and pathological populations.