Joint Learning of Full-structure Noise in Hierarchical Bayesian Regression Models

We consider the reconstruction of brain activity from electroencephalography (EEG). This inverse problem can be formulated as a linear regression with independent Gaussian scale mixture priors for both the source and noise components. Crucial factors influencing the accuracy of the source estimation...

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Veröffentlicht in:	IEEE transactions on medical imaging Jg. 43; H. 2; S. 1
Hauptverfasser:	Hashemi, Ali, Cai, Chang, Gao, Yijing, Ghosh, Sanjay, Muller, Klaus-Robert, Nagarajan, Srikantan S., Haufe, Stefan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.02.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Bayes methods Bayes Theorem Bayesian analysis Brain modeling Computer Simulation Covariance matrices Covariance matrix EEG EEG/MEG Brain Source Imaging Electroencephalography Electroencephalography - methods Gaussian noise Hierarchical Bayesian Learning Imaging Inverse problems Machine learning Magnetoencephalography - methods Majorization Minimization Manifolds Mathematical models Noise levels Observational learning Random noise Regression analysis Regression models Sparse Bayesian Learning Type-II Maximum-Likelihood
ISSN:	0278-0062, 1558-254X, 1558-254X
Online-Zugang:	Volltext
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