计算机科学
贝叶斯概率
正规化(语言学)
模式识别(心理学)
人工智能
变化(天文学)
脑磁图
相关性(法律)
脑电图
贝叶斯推理
算法
心理学
精神科
物理
天体物理学
政治学
法学
作者
Ke Liu,Zhu Liang Yu,Wei Wu,Zhenghui Gu,Yuanqing Li
标识
DOI:10.1016/j.neucom.2020.01.038
摘要
Estimating the extents and localizations of extended sources from noninvasive EEG/MEG signals is challenging. In this paper, we have proposed a fully data driven source imaging method, namely Variation Sparse Source Imaging based on Automatic Relevance Determination (VSSI-ARD), to reconstruct extended cortical activities. VSSI-ARD explores the sparseness of current sources on the variation domain by employing ARD prior under empirical Bayesian framework. With convex analysis, the sources are efficiently obtained by solving a series of reweighting L21-norm regularization problems with ADMM. By virtue of the iterative reweighting process and sparse signal processing techniques, VSSI-ARD gets rid of the small amplitude dipoles that are more probably outside the extent of underlying sources. With the sparsity enforced on the edges using ARD prior, the estimations show clear boundaries between active and background regions without subjective thresholds. Validation with both simulated and human experimental data indicates that VSSI-ARD not only estimates the localizations of sources, but also provides relatively useful and accurate information about the extents of cortical activities.
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