title: A generalized spatiotemporal covariance model for stationary background in analysis of MEG data.
abstract: Using a noise covariance model based on a single Kronecker product of spatial and temporal covariance in the spatiotemporal analysis of MEG data was demonstrated to provide improvement in the results over that of the commonly used diagonal noise covariance model. In this paper we present a model that is a generalization of all of the above models. It describes models based on a single Kronecker product of spatial and temporal covariance as well as more complicated multi-pair models together with any intermediate form expressed as a sum of Kronecker products of spatial component matrices of reduced rank and their corresponding temporal covariance matrices. The model provides a framework for controlling the tradeoff between the described complexity of the background and computational demand for the analysis using this model. Ways to estimate the value of the parameter controlling this tradeoff are also discussed.
authors.affiliation: Dept. of Comput. Sci., New Mexico Univ., Albuquerque, NM 87545, USA. pliz@cs.unm.edu
authors.forename: S M
authors.initials: SM
authors.lastname: Plis
authors.forename: D M
authors.initials: DM
authors.lastname: Schmidt
authors.forename: S C
authors.initials: SC
authors.lastname: Jun
authors.forename: D M
authors.initials: DM
authors.lastname: Ranken
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016014:Linear Models; D015225:Magnetoencephalography / Q000379:methods*; D008959:Models, Neurological; D019047:Phantoms, Imaging