Linear inverse solutions: Simulations from a realistic head model in MEG

Brain Topography

Volumn 18, Issue 2, 2005, Pages 87-99

  Soufflet, Laurent ^a,b   Boeijinga, Peter H ^a  

^a CENTRE HOSPITALIER   (France)

^b FORENAP   (France)

Author keywords

Inverse problem; MEG; Minimum norm; sLORETA

Indexed keywords

ARTICLE; DIPOLE; ELECTROMAGNETIC FIELD; ERROR; HEAD; MAGNETOENCEPHALOGRAPHY; MATHEMATICAL COMPUTING; NOISE REDUCTION; NORMAL DISTRIBUTION; PRIORITY JOURNAL; SIMULATION; STATISTICAL MODEL;

ALGORITHMS; COMPUTER SIMULATION; ELECTROENCEPHALOGRAPHY; HEAD; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; LINEAR MODELS; MAGNETOENCEPHALOGRAPHY; MODELS, ANATOMIC;

EID: 28844458087     PISSN: 08960267     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10548-005-0278-6     Document Type: Article

References (28)

1. Backus, G. and Gilbert, F. The resolving power of gross earth data. Geophys. J. R. Astr. Soc, 1968, 16: 169-205.
2. Coatanhay, A., Soufflet, L., Staner, L. and Boeijinga, P. EEG source identification: frequency analysis during sleep. C.R. Biologies, 2002, 325: 273-282.
3. Dale, A.M. and Sereno, M.I. Improved localisation of activity by combining EEG and MEG with MRI cortical surface reconstruction: A linear approach. J. Cognit. Neurosci., 1993, 5: 162-176.
4. Dale, A.M., Liu, A.K. and Fischl, B.R. Dynamic statistical parametric mapping: Combining fMRI and MEG for high-resolution imaging of cortical activity. Neuron, 2000: 55-67.
5. Ferguson, A.S., Zhang, X. and Stroink, G. A complete linear discretization for calculating the magnetic field using the boundary element method. IEEE Trans. Biomed. Eng., 1994, 41: 455-459.
6. Frei, E., Gamma, A., Pascual-Marqui, R., Lehmann, D., Hell, D. and Vollenweider, F.X. Localization of MDMA-Induced Brain Activity in Healthy Volunteers Using Low Resolution Brain Electromagnetic Tomography (LORETA). Human Brain Mapping, 2001, 14: 152-165.
7. Fuchs, M., Wagner, M. and Kastner, J. Boundary element method volume conductor models for EEG source reconstruction. Electroenceph. Clin. Neurophysiol., 2001, 112: 1400-1407.
8. Grave de Peralta Menendez, R., Hauk, O., Gonzalez Andino, S., Vogt, H. and Michel, C. Linear inverse solutions with optimal resolution kernels applied to electromagnetic tomography. Human Brain Mapping, 1997, 5: 454-467.
9. Grave de Peralta Menendez, R. and Gonzalez Andino, S.L. A critical analysis of linear inverse solutions. IEEE Trans. Biomed. Eng., 1998, 4: 440-448.
10. Grave de Peralta Menendez, R. and Gonzalez Andino, S.L. Discussing the capabilities of laplacian minimization. Brain Topography, 2000, 13: 97-104.
11. Grave de Peralta Menendez, R. and Gonzalez Andino, S.L. Comparison of algorithms for the localization of focal sources: evaluation with simulated data and analysis of experimental data. Int. J. Bioelectromagnetism, 2002, Vol. 4, No. 1.
12. Grave de Peralta Menendez, R., Murray M.M., Michel, C.M., Martuzzi, R. and Gonzalez Andino, S.L. Electrical neuroimaging based on biophysical constraints. Neuroimage, 2004, 21: 527-539.
13. Greenblatt, R.E., Ossadtchi, A. and Pflieger, M.E. Local linear estimators for the bioelectromagnetic inverse problem. IEEE Trans. Signal Processing, 2005, 53: 3403-3412.
14. Hämäläinen, M.S. and Ilmoniemi, R.J. Interpolating measured magnetic fields of the brain: Estimates of current distributions. Tech. Rep. TKK-F-A559, Helsinki Univ. Technology, 1984.
15. Hämäläinen, M.S. and Sarvas, J. Realistic conductivity geometry model of the human head for interpretation of neuromagnetic data. IEEE Trans. Biomed. Eng., 1989, 36: 165-171.
16. Leahy, R.M., Mosher, J.C. and Phillips, J.W. A comparative study of minimum norm inverse methods for MEG imaging. Proceedings of the 10th International Conference of Biomagnetism, 1996.
17. Lawson, C.L. and Hanson, R.J. Solving least squares problems. Prentice Hall, Inc., Englewood Cliffs, New Jersey, 1974.
18. Michel, C.M, Murray, M.M., Lantz, G., Gonzalez, S., Spinelli, L. and Grave de Peralta, R. EEG source imaging. Clinical Neurophysiology, 2004, 115: 2195-2222.
19. Moore, E.H. On the reciprocal of the general algebraic matrix. Bull. Amer. Math. Soc., 1920, 26: 394-395.
20. Pascual-Marqui, R.D., Michel, C.M. and Lehmann, D. Low resolution electromagnetic tomography: A new method for localizing electrical activity in the brain. Int. J. Psychophysiol., 1994, 18: 49-65.
21. Pascual-Marqui, R.D. Reply to comments by Hämäläinen, Ilmoniemi and Nunez. In: W. Skrandies (Ed.), Source Localization: Continuing Discussion of the Inverse Problem. ISBET Newsletter, 1995, 6: 16-28.
22. Pascual-Marqui, R.D. Review of methods for solving the EEG inverse problem. Int. J. Bioelectromagnetism, 1999, Vol. 1, No. 1.
23. Pascual-Marqui, R.D. Standardized Low-Resolution Brain Electromagnetic Tomography (sLORETA): Technical Details. Methods Find Exp. Clin. Pharmacol., 2002, 24D: 5-12.
24. Penrose, A. A generalized inverse for matrices. In Proc. Cambridge Philos. Soc., 1955, 51: 406-413.
25. Philipps, C., Rugg, M.D. and Friston, K.J. Systematic regularization of linear inverse solutions of the EEG source localization problem. Neuroimage, 2002, 17: 287-301.
26. Soufflet, L. and Borouchaki, H. Calcul des potentiels électriques et des champs magnétiques par la méthode des intégrales de surface. Revue européenne des éléments finis, 2002, 11: 81-99.
27. Tikhonov, A.N. and Arsenin, V.Y. Solutions of Ill-Posed Problems. New York, Wiley, 1977.
28. Wagner, M., Fuchs, M. and Kastner, J. Evaluation of sLORETA in the presence of noise and multiple sources. Brain Topography, 2004, 16: 277-280.