1. MfD EEG/MEG Source Localization 4 th Feb 2009 Maro Machizawa Himn Sabir Expert: Vladimir Litvak

2. Inverse problem 1.Existence 2.Unicity 3.Stability

3. 1.Existence 2.Unicity 3.Stability Inverse problem

4. 1.Existence 2.Unicity 3.Stability Inverse problem Introduction of prior knowledge is needed

5. Spatio-temporal modeling

6. Spatio-temporal modeling – step 1 Load EEG/MEG file

7. Spatio-temporal modeling – step 2 Name the analysis (optional)

8. Spatio-temporal modeling – step 3 Create/load meshes Bigger the parameter, better the resolution of the results

9. Spatio-temporal modeling – step 4 Coregister fiducial points with MRI Choose either of methods to coregister –“select” from default locations (at FIL) –“type” MNI coordinates directory –“click” manually each fiducial point from MRI images

10. Spatio-temporal modeling – step 4 Coregister fiducial points with MRI

11. Spatio-temporal modeling – step 5 Forward model

12. Spatio-temporal modeling – step 5 Bayesian model inversion

13. Spatio-temporal modeling – step 5 Invert: alternative models GS (greedy search: default): –iteratively add constraints (priors) ARD (automatic relevance determination): –iteratively remove irrelevant constraints COH (coherence): –LORETA-like smooth prior IID (independent identically distributed): –minimum norm

14. Spatio-temporal modeling – step 5 Invert: alternative models The bigger the number, the better the model -1893 -1913

15. Spatio-temporal modeling – step 5 Invert: visualization options 1 digit (ms): map on that time(ms) 2 digits (ms): video during the period 3 digits (x y z): max. voxel on that MNI coordinate

16. Spatio-temporal modeling – step 6 Window : Induced: localization on each single trial then averaged Evoked: localization on already averaged data INDUCED IMAGE

17. Spatio-temporal modeling – step 7 Image

19. Group analysis: same analysis on multiple subjects

20. (Optional step5) Variational Bayes Equivalent Current Dipole

21. Optional: time-voltage display