Estimating vector fields using sparse basis field expansions

Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference

Volumn , Issue , 2009, Pages 617-624

  Haufe, Stefan ^a,b   Nikulin, Vadim V ^c,d   Ziehe, Andreas ^a,b   Müller, Klaus Robert ^a,b,d   Nolte, Guido ^a,b  

^a TECHNISCHE UNIVERSITÄT BERLIN   (Germany)

^b FRAUNHOFER FIRST   (Germany)

^c CHARITÉ UNIVERSITÄTSMEDIZIN BERLIN   (Germany)

^d BERNSTEIN CENTER FOR COMPUTATIONAL NEUROSCIENCE   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

BASE FUNCTION; CURRENT SOURCES; ROTATIONAL INVARIANCES; SECOND-ORDER CONE PROGRAMMING; SPARSE BASE; STATE OF THE ART; VECTOR FIELDS;

INVERSE PROBLEMS;

EID: 77954643957     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. F.R. Bach, G.R.G. Lanckriet, and M.I. Jordan. Multiple kernel learning, conic duality and the SMO algorithm. In Proceedings of the Twenty-first International Conference on Machine Learning, 2004.
2. M. Grant, S. Boyd, and Y. Ye. CVX: Matlab Software for Disciplined Convex Programming, October 2006. http://www.stanford.edu/~boyd/cvx/, Version 1.0RC.
3. S. Haufe, V.V. Nikulin, A. Ziehe, K.-R. Müller, and G. Nolte. Combining sparsity and rotational invariance in EEG/MEG source reconstruction. NeuroImage, 42(2):26-738, 2008.
4. C.J. Holmes, R. Hoge, L. Collins, R. Woods, A.W. Toga, and A.C. Evans. Enhancement of MR images using registration for signal averaging. J. Comput. Assist. Tomogr., 22(2):324-333, 1998. (Pubitemid 28155214)
5. J. Huttunen, S. Komssi, and L. Lauronen. Spatial dynamics of population activities at S1 after median and ulnar nerve stimulation revisited: An MEG study. NeuroImage, 32:1024-1031, 2006. (Pubitemid 44278671)
6. M.S. Lobo, L. Vandenberghe, S. Boyd, and H. Lebret. Applications of second-order cone programming. Lin. Alg. Appl., 284:193-228, 1998.
7. K. Matsuura and Y. Okabe. Selective minimum-norm solution of the biomagnetic inverse problem. IEEE Trans. Biomed. Eng., 42:608-615, 1995.
8. F.A. Mussa-Ivaldi. From basis functions to basis fields: vector field approximation from sparse data. Biol. Cybern., 67:479-489, 1992.
9. G. Nolte and G. Dassios. Analytic expansion of the EEG lead field for realistic volume conductors. Phys. Med. Biol., 50:3807-3823, 2005. (Pubitemid 41129152)
10. R.D. Pascual-Marqui. Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. Meth. Find. Exp. Clin. Pharmacol., 24(1):5-12, 2002. (Pubitemid 35462350)
11. R.D. Pascual-Marqui, C.M. Michel, and D. Lehmann. Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain. Int. J. Psychophysiol., 18:49-65, 1994. (Pubitemid 24344515)
12. J.F. Sturm. Using SeDuMi 1.02, a MATLAB toolbox for optimization over symmetric cones. Optim. Method. Softw., 11-12:625-653, 1999.
13. A. Tarantola. Inverse Problem Theory and Model Parameter Estimation. SIAM, Philadelphia, 2005.
14. R. Tibshirani. Regression shrinkage and selection via the lasso. J. Roy. Stat. Soc. B Meth., 58(1):267-288, 1996.
15. R. Tomioka and S. Haufe. Combined classification and channel/basis selection with L1-L2 regularization with application to P300 speller system. In Proceedings of the 4th International Brain-Computer Interface Workshop and Training Course 2008. Verlag der Technischen Universität Graz, 2008.
16. M. Vega-Hernández, E. Martínez-Montes, J.M. Sánchez-Bornot, A. Lage-Castellanos, and P.A. Valdés-Sosa. Penalized least squares methods for solving the EEG inverse problem. Stat. Sinica, 2008. In press.
17. D.P. Wipf and B.D. Rao. An empirical bayesian strategy for solving the simultaneous sparse approximation problem. IEEE Trans. Signal Proces., 55(7):3704-3716, 2007. (Pubitemid 47018850)
18. M. Yuan and Y. Lin. Model selection and estimation in regression with grouped variables. J. Roy. Stat. Soc. B Meth., 68(1):49-67, 2006. (Pubitemid 43415335)