EEG Source Reconstruction using Sparse Basis Function Representations

Sofie Therese Hansen, Lars Kai Hansen

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Abstract

State of the art performance of 3D EEG imaging is based on reconstruction using spatial basis function representations. In this work we augment the Variational Garrote (VG) approach for sparse approximation to incorporate spatial basis functions. As VG handles the bias variance trade-off with cross-validation this approach is more automated than competing approaches such as Multiple Sparse Priors (Friston et al., 2008) or Champagne (Wipf et al., 2010) that require manual selection of noise level and auxiliary signal free data, respectively. Finally, we propose an unbiased estimator of the reproducibility of the reconstructed activation time course based on a split-half resampling protocol.
Original languageEnglish
Title of host publicationProceedings of 4th International Workshop on Pattern Recognition in Neuroimaging
Number of pages4
PublisherIEEE
Publication date2014
ISBN (Print)978-1-4799-4149-0
Publication statusPublished - 2014
Event4th International Workshop on Pattern Recognition in Neuroimaging - Max Planck Institutes, Tübingen, Germany
Duration: 4 Jun 20146 Jun 2014
Conference number: 4
http://mlin.kyb.tuebingen.mpg.de/prni2014/prni2014.html

Workshop

Workshop4th International Workshop on Pattern Recognition in Neuroimaging
Number4
LocationMax Planck Institutes
CountryGermany
CityTübingen
Period04/06/201406/06/2014
Internet address

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