Unification of sparse Bayesian learning algorithms for electromagnetic brain imaging with the majorization minimization framework

Unification of sparse Bayesian learning algorithms for electromagnetic brain imaging with the majorization minimization framework

Methods for electro- or magnetoencephalography (EEG/MEG) based brain source imaging (BSI) using sparse Bayesian learning (SBL) have been demonstrated to achieve excellent performance in situations with low numbers of distinct active sources, such as event-related designs. This paper extends the theo...

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Bibliographic Details
Main Authors: Ali Hashemi, Chang Cai, Gitta Kutyniok, Klaus-Robert Müller, Srikantan S. Nagarajan, Stefan Haufe
Format: Article
Language:English
Published: Elsevier 2021-10-01
Series:NeuroImage
Subjects:
Electro-/magnetoencephalography
Brain source imaging
Type I/II Bayesian learning
Non-convex
Majorization-Minimization
Noise learning
Online Access:http://www.sciencedirect.com/science/article/pii/S1053811921005851

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http://www.sciencedirect.com/science/article/pii/S1053811921005851

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