文章基本信息

标题：A spatio-temporal solution for the EEG/MEG inverse problem using group penalization methods
本地全文：下载
作者：Zhimin Li ; Tian Siva Tian
期刊名称：Statistics and Its Interface
印刷版ISSN：1938-7989
电子版ISSN：1938-7997
出版年度：2011
卷号：4
期号：4
页码：521-533
DOI：10.4310/SII.2011.v4.n4.a10
出版社：International Press
摘要：The inverse problem encountered in electroencephalography (EEG) and magnetoencephalography (MEG) studies refers to estimating neural activity given limited scalprecorded data. We propose a spatio-temporal solution using group penalization approaches. This proposed method is based on the assumption that the underlying sources of EEG/MEG measurements are smooth in the temporal domain, and focal in the spatial domain. It transforms the spatio-temporal problem to a high-dimensional linear regression problem with grouped predictors using a basis expansion. Then an iterative group elastic net algorithm is utilized to localize and estimate the source time courses. The proposed approach is shown to be effective on simulations and human MEG studies.
关键词：inverse problem; spatio-temporal data; group elastic net; EEG/MEG