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An Evaluation of EEG Scanner’s Dependence on the Imaging Technique, Forward Model Computation Method, and Array Dimensionality

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Authors:
  • Stahlhut, Carsten ;
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    Department of Informatics and Mathematical Modeling, Technical University of Denmark
  • Attias, Hagai Thomas ;
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    Convex Imaging
  • Stopczynski, Arkadiusz ;
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    Department of Informatics and Mathematical Modeling, Technical University of Denmark
  • Petersen, Michael Kai ;
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    Orcid logo0000-0002-2156-9226
    Department of Informatics and Mathematical Modeling, Technical University of Denmark
  • Larsen, Jakob Eg ;
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    Orcid logo0000-0002-7461-9901
    Department of Informatics and Mathematical Modeling, Technical University of Denmark
  • Hansen, Lars Kai
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    Orcid logo0000-0003-0442-5877
    Department of Informatics and Mathematical Modeling, Technical University of Denmark
DOI:
10.1109/EMBC.2012.6346235
Abstract:
EEG source reconstruction involves solving an inverse problem that is highly ill-posed and dependent on a generally fixed forward propagation model. In this contribution we compare a low and high density EEG setup’s dependence on correct forward modeling. Specifically, we examine how different forward models affect the source estimates obtained using four inverse solvers Minimum-Norm, LORETA, Minimum-Variance Adaptive Beamformer, and Sparse Bayesian Learning.
ISBN:
9781424441204, 9781424441198
Type:
Conference paper
Language:
English
Published in:
Engineering in Medicine and Biology Society (embc), Annual International Conference of the Ieee, 2012, p. 1538-1541
Main Research Area:
Science/technology
Publication Status:
Published
Series:
I E E E Engineering in Medicine and Biology Society. Conference Proceedings
Review type:
Peer Review
Conference:
34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2012
Publisher:
IEEE
Submission year:
2012
Scientific Level:
Scientific
ID:
232632369

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