DocumentCode :
3107816
Title :
Sparse Source EEG Imaging with the Variational Garrote
Author :
Hansen, Sofie Therese ; Stahlhut, C. ; Hansen, Lars Kai
Author_Institution :
Dept. of Appl. Math. & Comput. Sci., Tech. Univ. of Denmark, Lyngby, Denmark
fYear :
2013
fDate :
22-24 June 2013
Firstpage :
106
Lastpage :
109
Abstract :
EEG imaging, the estimation of the cortical source distribution from scalp electrode measurements, poses an extremely ill-posed inverse problem. Recent work by Delorme et al. (2012) supports the hypothesis that distributed source solutions are sparse. We show that direct search for sparse solutions as implemented by the Variational Garrote (Kappen, 2011) provides excellent estimates compared with other widely used schemes, is computationally attractive, and by its separation of ´where´ and ´what´ degrees of freedom paves the road for the introduction of genuine prior information.
Keywords :
electroencephalography; medical image processing; cortical source distribution; scalp electrode measurements; sparse source EEG imaging; variational garrote; Brain modeling; Data models; Electroencephalography; Imaging; Inverse problems; Noise; Real-time systems; EEG; Imaging; LASSO; Sparse Bayesian Modeling; Sparsity; Variational Garrote;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition in Neuroimaging (PRNI), 2013 International Workshop on
Conference_Location :
Philadelphia, PA
Type :
conf
DOI :
10.1109/PRNI.2013.36
Filename :
6603568
Link To Document :
بازگشت