title: STRAPS: A Fully Data-Driven Spatio-Temporally Regularized Algorithm for M/EEG Patch Source Imaging.
abstract: For M/EEG-based distributed source imaging, it has been established that the L2-norm-based methods are effective in imaging spatially extended sources, whereas the L1-norm-based methods are more suited for estimating focal and sparse sources. However, when the spatial extents of the sources are unknown a priori, the rationale for using either type of methods is not adequately supported. Bayesian inference by exploiting the spatio-temporal information of the patch sources holds great promise as a tool for adaptive source imaging, but both computational and methodological limitations remain to be overcome. In this paper, based on state-space modeling of the M/EEG data, we propose a fully data-driven and scalable algorithm, termed STRAPS, for M/EEG patch source imaging on high-resolution cortices. Unlike the existing algorithms, the recursive penalized least squares (RPLS) procedure is employed to efficiently estimate the source activities as opposed to the computationally demanding Kalman filtering/smoothing. Furthermore, the coefficients of the multivariate autoregressive (MVAR) model characterizing the spatial-temporal dynamics of the patch sources are estimated in a principled manner via empirical Bayes. Extensive numerical experiments demonstrate STRAPS's excellent performance in the estimation of locations, spatial extents and amplitudes of the patch sources with varying spatial extents.
authors.affiliation: College of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, P. R. China.
authors.forename: Ke
authors.initials: K
authors.lastname: Liu
authors.forename: Zhu Liang
authors.initials: ZL
authors.lastname: Yu
authors.forename: Wei
authors.initials: W
authors.lastname: Wu
authors.forename: Zhenghui
authors.initials: Z
authors.lastname: Gu
authors.forename: Yuanqing
authors.initials: Y
authors.lastname: Li
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001931:Brain Mapping / Q000379:methods; D002540:Cerebral Cortex / Q000502:physiology*; D004569:Electroencephalography; D006801:Humans; D015225:Magnetoencephalography; D015233:Models, Statistical; D012815:Signal Processing, Computer-Assisted
keywords: Bayesian inference; M/EEG source imaging; spatio-temporally regularized algorithm