Multiple sparse priors for the M/EEG inverse problem
Multiple sparse priors for the M/EEG inverse problem
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DOI：
10.1016/j.neuroimage.2007.09.048
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发表时间：
2008-02-01
期刊：
影响因子：
5.7
通讯作者：
Mattout, Jeremie
中科院分区：
文献类型：
--
作者：
Friston, Karl J.;Harrison, Lee;Mattout, Jeremie
本文描述了一种将分层贝叶斯或经验贝叶斯应用于脑电和脑磁图(EEG和MEG)中的分布式源重构问题。关键的贡献是自动选择具有紧凑空间支持的多个皮质来源，这些支持是根据经验先验指定的。这消除了使用具有特定形式(例如，平滑或最小范数)或具有空间结构(例如，基于深度约束或功能磁共振成像结果的先验)的先验的需要。此外，该反演方案允许对由等效电流偶极子(ECD)模型强制实施的那种分布式源进行稀疏解。这意味着该方法根据数据自动选择稀疏或分布式模型。将该方案与贝叶斯解的传统应用进行了比较，以量化性能的改善。(C)2007 Elsevier Inc.保留所有权利。
This paper describes an application of hierarchical or empirical Bayes to the distributed source reconstruction problem in electro- and magnetoencephalography (EEG and MEG). The key contribution is the automatic selection of multiple cortical sources with compact spatial support that are specified in terms of empirical priors. This obviates the need to use priors with a specific form (e.g., smoothness or minimum norm) or with spatial structure (e.g., priors based on depth constraints or functional magnetic resonance imaging results). Furthermore, the inversion scheme allows for a sparse solution for distributed sources, of the sort enforced by equivalent current dipole (ECD) models. This means the approach automatically selects either a sparse or a distributed model, depending on the data. The scheme is compared with conventional applications of Bayesian solutions to quantify the improvement in performance. (c) 2007 Elsevier Inc. All rights reserved.