Two probabilistic algorithms for MEG/EEG source reconstruction

Johanna M. Zumer*, Hagai T. Attias, Kensuke Sekihara, Srikantan S. Nagarajan

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We have developed two algorithms for source imaging from MEG/EEG data. Contribution to sensor data from a source at a particular voxel is expressed as the product of a known lead field and temporal basis functions with unknown coefficients. Temporal basis functions are in turn estimated from data. The first algorithm models activity outside the voxel of interest by a full-rank covariance matrix and estimates unknowns by maximizing the likelihood. The second algorithm parameterizes activity outside the voxel of interest as a linear mixture of a set of unknown Gaussian factors plus Gaussian sensor noise and estimates all unknown quantities using an Expectation-Maximization (EM) algorithm. In both cases, the source image map is the likelihood of a dipole source at each voxel. Performance in simulations and real data demonstrate significant improvement over existing source localization methods.

Original languageEnglish
Title of host publication2006 3rd IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro - Proceedings
PublisherIEEE
Pages940-943
Number of pages4
Volume2006
ISBN (Print)0780395778, 9780780395770
DOIs
Publication statusPublished - 8 May 2006
Event2006 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Arlington, VA, United States
Duration: 6 Apr 20069 Apr 2006

Conference

Conference2006 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro
CountryUnited States
CityArlington, VA
Period6/04/069/04/06

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Zumer, J. M., Attias, H. T., Sekihara, K., & Nagarajan, S. S. (2006). Two probabilistic algorithms for MEG/EEG source reconstruction. In 2006 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings (Vol. 2006, pp. 940-943). [162574] IEEE. https://doi.org/10.1109/ISBI.2006.1625074