期刊
STATISTICS IN MEDICINE
卷 26, 期 21, 页码 3886-3910出版社
WILEY
DOI: 10.1002/sim.2941
关键词
magnetoencephalography; electroencephalography; graphical models
类别
资金
- NIDCD NIH HHS [R01 DC004855, R01DC006435, R01 DC006435, R01DC004855] Funding Source: Medline
Magnetoencephalography (MEG) and electroencephalography (EEG) sensor measurements are often contaminated by several interferences such as background activity from outside the regions of interest, by biological and non-biological artifacts, and by sensor noise. Here, we introduce a probabilistic graphical model and inference algorithm based on variational-B-ayes expectation-maximization for estimation of activity of interest through interference suppression. The algorithm exploits the fact that electromagnetic recording data can often be partitioned into baseline periods, when only interferences are present, and active time periods, when activity of interest is present in addition to interferences. This algorithm is found to be robust and efficient and significantly superior to many other existing approaches on real and simulated data. Copyright (c) 2007 John Wiley & Sons, Ltd.
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