期刊
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
卷 66, 期 9, 页码 2457-2469出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBME.2018.2890291
关键词
EEG/MEG source imaging; matrix factorization; variational bayesian inference; empirical bayesian
资金
- National Natural Science Foundation of China [61703065, 61836003, 61573150, 61573152, 61876063, 91420302, 61403085]
- Chongqing Research Program of Application Foundation and Advanced Technology [cstc2018jcyjAX0151]
- Science and Technology Research Program of Chongqing Municipal Education Commission [KJQN201800612]
- Common Key Technology Innovation Special of Key Industries [cstc2017zdcy-zdyfX0001]
Accurate estimation of the locations and extents of neural sources from electroencephalography and magnetoencephalography (E/MEG) is challenging, especially for deep and highly correlated neural activities. In this study, we proposed a new fully data-driven source imaging method, source imaging based on spatio-temporal basis function (SI-STBF), which is built upon a Bayesian framework, to address this issue. The SI-STBF is based on the factorization of a source matrix as a product of a sparse coding matrix and a temporal basis function (TBF) matrix, which includes a few TBFs. The prior of the TBF is set in the empirical Bayesian manner. Similarly, for the spatial constraint, the SI-STBF assumes the prior covariance of the coding matrix as a weighted sum of several spatial covariance components. Both the TBFs and the coding matrix are learned from E/MEG simultaneously through variational Bayesian inference. To enable inference on high-resolution source space, we derived a scalable algorithm using convex analysis. The performance of the SI-STBF was assessed using both simulated and experimental E/MEG recordings. Compared with L-2-norm constrained methods, the SI-STBF is superior in reconstructing extended sources with less spatial diffusion and less localization error. By virtue of the spatio-temporal factorization of source matrix, the SI-STBF also produces more accurate estimations than spatial-only constraint method for high correlated and deep sources.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据