Space-time event sparse penalization for magneto-/electroencephalography

NeuroImage

Volumn 46, Issue 4, 2009, Pages 1066-1081

  Bolstad, Andrew ^a   Veen, Barry Van ^a   Nowak, Robert ^a  

^a University of Wisconsin Madison   (United States)

Author keywords

M EEG Inverse problem; M EEG source localization; Spatio temporal priors

Indexed keywords

ALGORITHM; ARTICLE; BAYES THEOREM; ELECTROENCEPHALOGRAM; ELECTROENCEPHALOGRAPHY; MAGNETOENCEPHALOGRAPHY; MATHEMATICAL ANALYSIS; PRIORITY JOURNAL; SIMULATION;

ALGORITHMS; BRAIN MAPPING; COMPUTER SIMULATION; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETOENCEPHALOGRAPHY; MODELS, NEUROLOGICAL;

EID: 67349279224     PISSN: 10538119     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2009.01.056     Document Type: Article

References (46)

1. p-norm priors. NeuroImage 26 3 (2005) 870-884
2. Baillet S., and Garnero L. A Bayesian approach to introducing anatomo-functional prior in the EEG/MEG inverse problem. IEEE Trans. Biomed. Eng. 44 5 (1997) 374-385
3. Baillet S., Mosher J., and Leahy R. Electromagnetic brain mapping. IEEE Signal Process. Mag. 18 6 (2001) 14-30
4. Bear M., Connor B., and Paradiso M. Neuroscience: Exploring the Brain. 2nd Ed (2001), Lippincott, Williams and Wilkins, Philadelphia, PA
5. Bolstad A., Van Veen B., and Nowak R. Magneto-/electroencephalography with space-time sparse priors. IEEE Statistical Signal Processing Workshop (2007), Madison, WI 190-194 August
6. Brewer J. Kronecker products and matrix calculus in system theory. IEEE Trans. Circuits Syst. cas-25 9 (1978) 772-781
7. Candes E.J., and Tao T. Decoding by linear programming. IEEE Trans. Inf. Theory 51 12 (2005) 4203-4215
8. Cottereau B., Jerbi K., and Baillet S. Multiresolution imaging of meg cortical sources using an explicit piecewise model. NeuroImage 38 3 (2007) 439-451
9. Culham J.C., Cavina-Pratesi C., and Singhal A. The role of parietal cortex in visuomotor control: what have we learned from neuroimaging?. Neuropsychologia 44 13 (2006) 2668-2684
10. Dalal S., Guggisberg A., Edwards E., Sekihara K., Findlay A., Canolty R., Berger M., Knight R., Barbaro N., Kirsch H., and Nagarajan S. Five-dimensional neuroimaging: localization of the time-frequency dynamics of cortical activity. NeuroImage 40 4 (2008) 1686-1700
11. Daunizeau J., and Friston K. A mesostate-space model for EEG and MEG. NeuroImage 38 1 (2007) 67-81
12. Daunizeau J., Mattout J., Clonda D., Goulard B., Benali H., and Lina J. Bayesian spatio-temporal approach for EEG source reconstruction: conciliating ECD and distributed models. IEEE Trans. Biomed. Eng. 53 3 (2006) 503-516
13. Demiralp T., Ademoglu A., Istefanopulos Y., and Gülçür H. Analysis of event-related potentials (ERP) by damped sinusoids. Biol. Cybern. 78 (1998) 487-493
14. Dempster A., Laird N., and Rubin D. Maximum likelihood from incomplete data via the EM algorithm (with discussion). J. R. Stat. Soc., B 39 (1977) 1-38
15. Ding L., and He B. Sparse source imaging in EEG. Proceedings of NFSI and ICFBI (2007) October
16. Dogandžić A., and Nehorai A. Estimating evoked dipole responses in unknown spatially correlated noise with EEG/MEG arrays. IEEE Trans. Signal Process. 48 1 (2000) 13-25
17. Donoho D. De-noising by soft-thresholding. IEEE Trans. Inf. Theory 41 3 (1995) 613-627
18. Donoho D. For most large underdetermined systems of linear equations, the minimal ell-1 norm solution is also the sparsest solution. Commun. Pure Appl. Math. 59 6 (2006) 797-829
19. Effern A., Lehnertz K., Fernández G., Grunwald T., David P., and Elger C. Single trial analysis of event related potentials: non-linear de-noising with wavelets. Clin. Neurophysiol. 111 (2000) 2255-2263
20. Figueiredo M., and Nowak R. An EM algorithm for wavelet-based image restoration. IEEE Trans. Image Process. 12 8 (2003) 906-916
21. Gellman A., Carlin J., Stern H., and Rubin D. Bayesian Data Analysis. 2nd Ed. (2003), CRC Press, Boca Raton, FL
22. Gorodnitsky I., and Rao B. Sparse signal reconstruction from limited data using focuss: a re-weighted minimum norm algorithm. IEEE Trans. Signal Process 45 3 (1997)
23. Huang M., Dale A., Song T., Halgren E., Harrington D., Podgorny I., Canive J., Lewis S., and Lee R. Vector-based spatial-temporal minimum l1-norm solution for meg. NeuroImage 31 (2006) 1025-1037
24. Jeffs B., Leahy R., and Singh M. An evaluation of methods for neuromagnetic image reconstruction. IEEE Trans. Biomed. Eng. 34 (1987) 713-723
25. Limpiti T., Van Veen B., and Wakai R. Cortical patch basis model for spatially extended neural activity. IEEE Trans. Biomed. Eng. 53 9 (2006) 1740-1754
26. Malioutov D., Çetin M., and Wilsky A. A sparse signal reconstruction perspective for source localization with sensor arrays. IEEE Trans. Signal Process 53 8 (2005) 3010-3022
27. Matsuura K., and Okabe Y. Selective minimum-norm solutions of the biomagnetic inverse problem. IEEE Trans. Biomed. Eng. 42 6 (1995) 608-615
28. Matsuura K., and Okabe Y. A robust reconstruction of sparse biomagnetic sources. IEEE Trans. Biomed. Eng. 44 8 (1997) 720-726
29. Mosher J., Leahy R., Shattuck D., and Baillet S. MEG source imaging using multipolar expansions. Lecture Notes in Computer Science Vol. 1613 (1999), Proc. IPMI99, Springer-Verlag GmbH
30. Mosher J., Lewis P., and Leahy R. Multiple dipole modeling and localization from spatio-temporal MEG data. IEEE Trans. Biomed. Eng. 39 6 (1992) 541-557
31. Nolte G., and Curio G. Current multipole expansion to estimate lateral extent of neuronal activity: a theoretical analysis. IEEE Trans Biomed. Eng. 47 10 (2000) 1347-1355
32. Ou W., Hämäläinen M., and Golland P. A distributed spatio-temporal EEG/MEG inverse solver. NeuroImage 44 3 (2009) 932-946
33. 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. 18 (1994) 49-65
34. Rockafellar R.T. Convex Analysis (1970), Princeton University Press, Princeton, New Jersey
35. Scherg M., and von Cramon D. Two bilateral sources of the late AEP as identified by a spatio-temporal dipole model. Electroencephalogr. Clin. Neurophysiol. 62 (1985) 32-44
36. Trejo L., and Shensa M. Feature extraction of event-related potentials using wavelets: an application to human performance monitoring. Brain Lang. 66 (1999) 89-107
37. Tropp J. Algorithms for simultaneous sparse approximation. Part ii: Convex relaxation. Signal Process. 86 (2006) 589-602
38. Trujillo-Barreto N., Aubert-Vázquez E., and Penny W. Bayesian M/EEG source reconstruction with spatio-temporal priors. NeuroImage 39 1 (2008) 318-335
39. Turlach B., Venables W., and Wright S. Simultaneous variable selection. Technometrics 47 3 (2005) 349-363
40. Uutela K., Hämäläinen M., and Somersalo E. Visualization of magnetoencephalographic data using minimum current estimates. NeuroImage 10 (1999) 173-180
41. Van Veen B., Van Drongelen W., Yuchtman M., and Suzuki A. Localization of brain electrical activity via linearly constrained minimum variance spatial filtering. IEEE Trans. Biomed. Eng. 44 9 (1997) 867-880
42. Wagner M., Köhler T., Fuchs M., and Kastner J. An extended source model for current density reconstructions. In: Nenonen J., Ilmoniemi R., and Katila T. (Eds). Proceedings of the 12th International Conference on Biomagnetism (2000), Espoo: Helsinki Univ. of Technology, Espoo, Finland 749-752 August
43. Wagner M., Fuchs M., and Kastner J. Current density reconstructions and deviation scans using extended sources. Proceedings of the 13th International Conference on Biomagnetism (2002), Jena, Germany August
44. Wright S., Nowak R., and Figueiredo M. Sparse reconstruction by separable approximation. IEEE International Conference on Acoustics, Speech, and Signal Processing (2008), Las Vegas, NV Mar. 30-April. 4
45. Wu C. On the convergence properties of the EM algorithm. Ann. Stat. 11 (1983) 95-103
46. Yuan M., and Lin Y. Model selection and estimation in regression with grouped variables. J. R. Stat. Soc., B 68 (2006) 49-67