= 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/|Hauk, O., Wakeman, D.G., Henson, R.N. (2011)]] Comparison of noise-normalized minimum norm estimates for MEG analysis using multiple resolution metrics. ''Neuroimage 54:3, 1966-74''.