|
Size: 656
Comment:
|
← Revision 7 as of 2013-03-08 10:02:41 ⇥
Size: 1616
Comment: converted to 1.6 markup
|
| Deletions are marked like this. | Additions are marked like this. |
| Line 3: | Line 3: |
| 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 head model, source space, and a sensor configuration (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]). |
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 }}} --- 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''. |
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 Inverse Operator to these data, and check how well activation from these areas are localised. In order to use mne_simu, you need a forward solution created in MNE (e.g. from a real measurement). For more details, please refer to the MNE manual (V2.6, 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---
The following publications provide more information on the spatial resolution of MNE:
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.
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.