RT Journal Article
SR Electronic
T1 Joint Learning of Full-structure Noise in Hierarchical Bayesian Regression Models
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.11.28.470264
DO 10.1101/2021.11.28.470264
A1 Ali Hashemi
A1 Chang Cai
A1 Yijing Gao
A1 Sanjay Ghosh
A1 Klaus-Robert Müller
A1 Srikantan S. Nagarajan
A1 Stefan Haufe
YR 2021
UL http://biorxiv.org/content/early/2021/11/28/2021.11.28.470264.abstract
AB 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 accuracy of source estimation are not only the noise level but also its correlation structure, but existing approaches have not addressed estimation of noise covariance matrices with full structure. To address this shortcoming, we develop hierarchical Bayesian (type-II maximum likelihood) models for observations with latent variables for source and noise, which are estimated jointly from data. As an extension to classical sparse Bayesian learning (SBL), where across-sensor observations are assumed to be independent and identically distributed, we consider Gaussian noise with full covariance structure. Using the majorization-maximization framework and Riemannian geometry, we derive an efficient algorithm for updating the noise covariance along the manifold of positive definite matrices. We demonstrate that our algorithm has guaranteed and fast convergence and validate it in simulations and with real MEG data. Our results demonstrate that the novel framework significantly improves upon state-of-the-art techniques in the real-world scenario where the noise is indeed non-diagonal and fully-structured. Our method has applications in many domains beyond biomagnetic inverse problems.Competing Interest StatementThe authors have declared no competing interest.