Quantifying time-varying sources in magnetoencephalography -- a discrete approach

DOI10.1214/19-AOAS1321zbMath1475.62272arXiv1908.03926MaRDI QIDQ2044260

Zengyan Fan, William F. Eddy, Zhigang Yao, Masahito Hayashi

Publication date: 4 August 2021

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1908.03926

zbMATH Keywords

expectation-maximizationspatiotemporal modelsource localizationdiscrete posterior distributionMEG inverse problem

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Image analysis in multivariate analysis (62H35)

Cites Work

• A statistical approach to the inverse problem in magnetoencephalography
• State-space solutions to the dynamic magnetoencephalography inverse problem using high performance computing
• Sequential Monte Carlo Methods for Dynamic Systems
• Estimating the Number of Sources in Magnetoencephalography Using Spiked Population Eigenvalues
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