title: [Electroencephalography inverse problem by subspace decomposition of the fourth-order cumulant matrix].
abstract: It is an important topic in electroencephalography (EEG) research to localize the EEG activity sources from the scalp recordings. In this paper, based on the fourth-order cumulant matrix, a new sub-space decomposition algorithm is proposed for the EEG inverse problem. As the second-order moments (cumulants) has the drawback of being sensitive to the noise covariance. Using the fourth-order cumulants we need not know the noise covariances, as long as the noise is Gaussian. Computer simulation study on a three-layer concentric sphere head model shows its better performance than the two-order cumulate method in depressing the spatial coherent Gaussian noise.
authors.affiliation: Department of Automation University of Electronic Science and Technology, Chengdu 610054.
authors.forename: D
authors.initials: D
authors.lastname: Yao
mesh_terms: D000465:Algorithms; D016477:Artifacts; D004569:Electroencephalography; D008954:Models, Biological; D012815:Signal Processing, Computer-Assisted