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An evaluation of EEG scanner's dependence on the imaging technique, forward model computation method, and array dimensionality
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...
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Published in: | 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2012-01, Vol.2012, p.1538-1541 |
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container_title | 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society |
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creator | Stahlhut, C. Attias, H. T. Stopczynski, A. Petersen, M. K. Larsen, J. E. Hansen, L. K. |
description | 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. |
doi_str_mv | 10.1109/EMBC.2012.6346235 |
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source | IEEE Electronic Library (IEL) Conference Proceedings |
subjects | Biological system modeling Brain modeling Computational modeling Electroencephalography Image reconstruction Imaging Sensors |
title | An evaluation of EEG scanner's dependence on the imaging technique, forward model computation method, and array dimensionality |
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