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.
Original language | English |
---|---|
Title of host publication | 2013 International Workshop on Pattern Recognition in Neuroimaging (PRNI) |
Publisher | IEEE |
Publication date | 2013 |
Pages | 106-109 |
ISBN (Print) | 978-0-7695-5061-9 |
DOIs | |
Publication status | Published - 2013 |
Event | 3rd International Workshop on Pattern Recognition in NeuroImaging (PRNI 2013) - Philadelphia, PA, United States Duration: 22 Jun 2013 → 24 Jun 2013 http://www.prni.org/ |
Conference
Conference | 3rd International Workshop on Pattern Recognition in NeuroImaging (PRNI 2013) |
---|---|
Country/Territory | United States |
City | Philadelphia, PA |
Period | 22/06/2013 → 24/06/2013 |
Internet address |
Keywords
- EEG
- Imaging
- Variational Garrote
- LASSO
- Sparse Bayesian Modeling
- Sparsity