用于脑磁图/脑电图的时空事件稀疏惩罚法
Space-time event sparse penalization for magneto-/electroencephalography.
作者信息
Bolstad Andrew, Van Veen Barry, Nowak Robert
机构信息
Department of Electrical and Computer Engineering, University of Wisconsin, Madison, WI 53715, USA.
出版信息
Neuroimage. 2009 Jul 15;46(4):1066-81. doi: 10.1016/j.neuroimage.2009.01.056. Epub 2009 Feb 6.
Abstract
This article presents a new spatio-temporal method for M/EEG source reconstruction based on the assumption that only a small number of events, localized in space and/or time, are responsible for the measured signal. Each space-time event is represented using a basis function expansion which reflects the most relevant (or measurable) features of the signal. This model of neural activity leads naturally to a Bayesian likelihood function which balances the model fit to the data with the complexity of the model, where the complexity is related to the number of included events. A novel Expectation-Maximization algorithm which maximizes the likelihood function is presented. The new method is shown to be effective on several MEG simulations of neurological activity as well as data from a self-paced finger tapping experiment.
摘要
本文提出了一种用于脑磁图/脑电图源重建的新的时空方法，该方法基于这样一种假设：只有少数在空间和/或时间上局部化的事件对测量信号负责。每个时空事件都使用基函数展开来表示，该展开反映了信号最相关（或可测量）的特征。这种神经活动模型自然地引出了一个贝叶斯似然函数，该函数在模型与数据的拟合度和模型的复杂度之间取得平衡，其中复杂度与所包含事件的数量有关。提出了一种最大化似然函数的新型期望最大化算法。新方法在几个神经活动的脑磁图模拟以及来自自定节奏手指敲击实验的数据上被证明是有效的。
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