Simulate Your Own Data in MNE

You can use the MNE function mne_simu to produce your own EEG or MEG data, for different ROIs (Labels). You can then apply your [http://imaging.mrc-cbu.cam.ac.uk/meg/AnalyzingData/MNE_InverseOperator Inverse Operator] to these data, and check how well activation from these areas are localised. In order to use mne_simu, you need a [http://imaging.mrc-cbu.cam.ac.uk/meg/AnalyzingData/MNE_ForwardSolution forward solution] created in MNE (e.g. from a real measurement). For more details, please refer to the MNE manual ([http://www.nmr.mgh.harvard.edu/meg/manuals/MNE-manual-2.6.pdf V2.6], [http://www.nmr.mgh.harvard.edu/meg/manuals/MNE-manual-2.7.pdf V 2.7]).

Example:

mne_simu --meg \
         --fwd ${path}/${subjects[m]}_5-1L-MEG-fwd.fif \
         --label ${STCpath}/Label_Occ-lh.label \
         --label ${STCpath}/Label_Cent-lh.label \
         --label ${STCpath}/Label_Ins-lh.label \
         --out ${STCpath}/PubLabel-lh.fif

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The following publications provide more information on the spatial resolution of MNE:

[http://www.sciencedirect.com/science/article/pii/S1053811908007143 Molins, A., Stufflebeam, S. M., Brown, E. N., & Hamalainen, M. S. (2008)]. Quantification of the benefit from integrating MEG and EEG data in minimum l2-norm estimation. Neuroimage, 42(3), 1069-1077.

[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3018574/ O. Hauk, D.G. Wakeman, R.N. Henson (2011)] Comparison of noise-normalized minimum norm estimates for MEG analysis using multiple resolution metrics. Neuroimage 54:3, 1966-74.