Sparse Source EEG Imaging with the Variational Garrote

Sofie Therese Hansen; Carsten Stahlhut; Lars Kai Hansen

doi:10.1109/PRNI.2013.36

Sparse Source EEG Imaging with the Variational Garrote

Sofie Therese Hansen, Carsten Stahlhut, Lars Kai Hansen

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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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	https://doi.org/10.1109/PRNI.2013.36
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	http://www.prni.org/

Keywords

EEG
Imaging
Variational Garrote
LASSO
Sparse Bayesian Modeling
Sparsity

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10.1109/PRNI.2013.36

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title = "Sparse Source EEG Imaging with the Variational Garrote",

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 {\textquoteright}where{\textquoteright} and {\textquoteright}what{\textquoteright} degrees of freedom paves the road for the introduction of genuine prior information.",

keywords = "EEG, Imaging, Variational Garrote, LASSO, Sparse Bayesian Modeling, Sparsity",

author = "Hansen, {Sofie Therese} and Carsten Stahlhut and Hansen, {Lars Kai}",

year = "2013",

doi = "10.1109/PRNI.2013.36",

language = "English",

isbn = "978-0-7695-5061-9",

pages = "106--109",

booktitle = "2013 International Workshop on Pattern Recognition in Neuroimaging (PRNI)",

publisher = "IEEE",

address = "United States",

note = "3rd International Workshop on Pattern Recognition in NeuroImaging (PRNI 2013) ; Conference date: 22-06-2013 Through 24-06-2013",

url = "http://www.prni.org/",

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T1 - Sparse Source EEG Imaging with the Variational Garrote

AU - Hansen, Sofie Therese

AU - Stahlhut, Carsten

AU - Hansen, Lars Kai

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

KW - EEG

KW - Imaging

KW - Variational Garrote

KW - LASSO

KW - Sparse Bayesian Modeling

KW - Sparsity

U2 - 10.1109/PRNI.2013.36

DO - 10.1109/PRNI.2013.36

M3 - Article in proceedings

SN - 978-0-7695-5061-9

SP - 106

EP - 109

BT - 2013 International Workshop on Pattern Recognition in Neuroimaging (PRNI)

PB - IEEE

T2 - 3rd International Workshop on Pattern Recognition in NeuroImaging (PRNI 2013)

Y2 - 22 June 2013 through 24 June 2013

ER -

Sparse Source EEG Imaging with the Variational Garrote

Abstract

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Keywords

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