Bayesian inversion of EEG models
作者： J. Mattout , C. Phillips , K. Friston , J. Daunizeau
DOI: 10.1016/B978-012372560-8/50029-2
关键词: Bayesian probability 、 Hyperparameter 、 Maximum a posteriori estimation 、 Generative model 、 Covariance 、 Computer science 、 Expectation–maximization algorithm 、 Restricted maximum likelihood 、 Algorithm 、 Bayes' theorem 、 Data mining
摘要: In this chapter, we consider a generative model for evoked neuronal responses as observed with electroencephalography (EEG) and magnetoencephalography (MEG). Because of its linear hierarchical nature, can be estimated efficiently using empirical Bayes. Importantly, multiple constraints on the source distribution incorporated in terms variance components that are from data. A dual estimation is obtained via an expectation maximization (EM) scheme to give restricted maximum likelihood (ReML) estimate prior covariance (in hyperparameters) posteriori (MAP) sources. The Bayesian formalism yields generic approach reconstruction under constraints, which extended cover spatio-temporal models induced next chapter.
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