Recursive MUSIC: A framework for EEG and MEG source localization
Recursive MUSIC: A framework for EEG and MEG source localization
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DOI：
10.1109/10.725331
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发表时间：
1998-11-01
影响因子：
4.6
通讯作者：
Leahy, RM
中科院分区：
文献类型：
--
作者：
Mosher, JC;Leahy, RM
多信号分类（MUSIC）算法可用于从脑电（EEG）和脑磁图（MEG）数据中定位多个异步偶极子源。该算法通过三维（3-D）头部体积扫描单偶极子模型，并计算到估计信号子空间上的投影。为了定位源，用户必须在头部体积中搜索投影度量中的多个局部峰值。在这里，我们描述了这种方法的扩展，我们称之为递归MUSIC（R-MUSIC）。这个新的过程自动提取的源的位置，通过递归使用子空间投影。新方法还能够通过使用时空独立的地形（IT）模型定位同步源。该模型将源定义为一个或多个具有单一时间过程的非旋转偶极子。在这个框架内，我们能够找到固定，旋转和同步偶极子。我在这里介绍的递归子空间投影过程使用典型或子空间相关性的度量作为模型子空间和数据子空间之间的多维形式的相关性分析。通过递归计算子空间相关性，我们建立了一个模型的来源，占一组给定的数据。我们在这里演示如何R-MUSIC可以轻松地提取多个异步偶极源，很难找到使用原始的MUSIC扫描。然后，我们证明R-MUSIC应用到更一般的IT模型，并显示固定，旋转和同步偶极子的组合结果。
The multiple signal classification (MUSIC) algorithm can be used to locate multiple asynchronous dipolar sources from electroencephalography (EEG) and magnetoencephalography (MEG) data. The algorithm scans a single-dipole model through a three-dimensional (3-D) head volume and computes projections onto an estimated signal subspace. To locate the sources, the user must search the head volume for multiple local peaks in the projection metric This task is time consuming and subjective. Here, we describe ari extension of this approach which we refer to as recursive MUSIC (R-MUSIC). This new procedure automatically extracts the locations of the sources through a recursive use of subspace projections. The new method is also able to locate synchronous sources through the use of a spatio-temporal independent topographies (IT) model. This model defines a source as one or more nonrotating dipoles with a single time course. Within this framework, we are able to locate fixed, rotating, and synchronous dipoles. The recursive subspace projection procedure that me introduce here uses the metric of canonical or subspace correlations as a multidimensional form of correlation analysis between the model subspace and the data subspace. By recursively computing subspace correlations, we build up a model for the sources which account for a given set of data. We demonstrate here how R-MUSIC can easily extract multiple asynchronous dipolar sources that are difficult to find using the original MUSIC scan. We then demonstrate R-MUSIC applied to the more general IT model and show results for combinations of fixed, rotating, and synchronous dipoles.