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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Detalles Bibliográficos
Autores principales: Hashemi, Ali, Cai, Chang, Kutyniok, Gitta, Müller, Klaus-Robert, Nagarajan, Srikantan S., Haufe, Stefan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8433122/
https://www.ncbi.nlm.nih.gov/pubmed/34182100
http://dx.doi.org/10.1016/j.neuroimage.2021.118309

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8433122/
https://www.ncbi.nlm.nih.gov/pubmed/34182100
http://dx.doi.org/10.1016/j.neuroimage.2021.118309

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