A Bayesian inverse solution using independent component analysis

被引：4
|
作者：
Puuronen, Jouni
Hyvarinen, Aapo [1 ,2 ]
机构：
[1] Univ Helsinki, Dept Comp Sci, FIN-00014 Helsinki, Finland
[2] Univ Helsinki, HIIT, FIN-00014 Helsinki, Finland
来源：
NEURAL NETWORKS | 2014年 / 50卷
关键词：
Independent component analysis; Electroencephalography; Magnetoencephalography; Bayesian methods; Inverse problem; Source localization; SOURCE SEPARATION; EEG; MAGNETOENCEPHALOGRAPHY; LOCALIZATION; ICA;
D O I：
10.1016/j.neunet.2013.10.007
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We present new results about the simultaneous linear inverse problems using independent component analysis (ICA), which can be used to separate the data into statistically independent components. The idea of using ICA in solving such inverse problems, especially in EEG/MEG context, has been a known topic for at least more than a decade, but the known results have been justified heuristically, and their relationships are not understood properly. Here we show how to obtain a Bayesian posterior for a spatial source distribution, by using an ICA demixing matrix as an input. The posterior enables us to rederive and reinterpret the previously known methods, and also provides completely new methods. (C) 2013 Elsevier Ltd. All rights reserved.
引用
收藏
页码：47 / 59
页数：13
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