Probabilistic algorithms for MEG/EEG source reconstruction using temporal basis functions learned from data.


We present two related probabilistic methods for neural source reconstruction from MEG/EEG data that reduce effects of interference, noise, and correlated sources. Both methods localize source activity using a linear mixture of temporal basis functions (TBFs) learned from the data. In contrast to existing methods that use predetermined TBFs, we compute TBFs from data using a graphical factor analysis based model [Nagarajan, S.S., Attias, H.T., Hild, K.E., Sekihara, K., 2007a. A probabilistic algorithm for robust interference suppression in bioelectromagnetic sensor data. Stat Med 26, 3886–3910], which separates evoked or event-related source activity from ongoing spontaneous background brain activity. Both algorithms compute an optimal weighting of these TBFs at each voxel to provide a spatiotemporal map of activity across the brain and a source image map from the likelihood of a dipole source at each voxel. We explicitly model, with two different robust parameterizations, the contribution from signals outside a voxel of interest. The two models differ in a trade-off of computational speed versus accuracy of learning the unknown interference contributions. Performance in simulations and real data, both with large noise and interference and/or correlated sources, demonstrates significant improvement over existing source localization methods.

Publication DOI:
Divisions: College of Health & Life Sciences > School of Psychology
College of Health & Life Sciences
Additional Information: © 2008, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Publication ISSN: 1095-9572
Last Modified: 03 Jun 2024 07:39
Date Deposited: 10 Jul 2019 15:40
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Related URLs: https://www.sci ... 1365?via%3Dihub (Publisher URL)
PURE Output Type: Article
Published Date: 2008-07-01
Authors: Zumer, JM (ORCID Profile 0000-0003-0419-3869)
Attias, HT
Sekihara, K
Nagarajan, SS

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