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MfD EEG/MEG Source Localization 4 th Feb 2009

MfD EEG/MEG Source Localization 4 th Feb 2009. Maro Machizawa Himn Sabir Expert: Vladimir Litvak. Inverse problem. Existence Unicity Stability. Inverse problem. Existence Unicity Stability. Inverse problem. Existence Unicity Stability. Introduction of prior knowledge is needed.

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MfD EEG/MEG Source Localization 4 th Feb 2009

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  1. MfD EEG/MEG Source Localization4th Feb 2009 Maro Machizawa Himn Sabir Expert: Vladimir Litvak

  2. Inverse problem • Existence • Unicity • Stability

  3. Inverse problem • Existence • Unicity • Stability

  4. Inverse problem • Existence • Unicity • Stability Introduction of prior knowledge is needed

  5. Spatio-temporal modeling

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

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

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

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

  11. Spatio-temporal modeling – step 5Forward model

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

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

  15. Spatio-temporal modeling – step 5Invert: 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 6Window : Induced: localization on each single trial then averaged Evoked: localization on already averaged data INDUCED IMAGE

  17. Spatio-temporal modeling – step 7Image

  18. Group analysis: same analysis on multiple subjects

  19. (Optional step5)Variational Bayes Equivalent Current Dipole

  20. Optional: time-voltage display

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