基于递归应用(RAP) MUSIC的脑电和脑磁图源定位

J. Mosher, R. Leahy
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引用次数: 21

摘要

多信号表征(MUSIC)算法从脑电图(EEG)和脑磁图(MEG)数据中定位多个异步偶极源。从数据中估计信号子空间,然后通过三维头部体积扫描单个偶极子模型并计算在该子空间上的投影。为了定位源,用户必须在头部体积中搜索投影度量中的局部峰值。我们描述了这种方法的一个新扩展,我们称之为RAP(递归应用)MUSIC。这个新过程通过递归使用子空间投影自动提取源的位置,子空间投影使用主相关性度量作为模型子空间和数据子空间之间相关性分析的多维形式。偶极取向,一种形式的“多样化极化”,很容易提取使用相关的主向量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
EEG and MEG source localization using recursively applied (RAP) MUSIC
The multiple signal characterization (MUSIC) algorithm locates multiple asynchronous dipolar sources from electroencephalography (EEG) and magnetoencephalography (MEG) data. A signal subspace is estimated from the data, then the algorithm scans a single dipole model through a three-dimensional head volume and computes projections onto this subspace. To locate the sources, the user must search the head volume for local peaks in the projection metric. We describe a novel extension of this approach which we refer to as RAP (recursively applied) MUSIC. This new procedure automatically extracts the locations of the sources through a recursive use of subspace projections, which uses the metric of principal correlations as a multidimensional form of correlation analysis between the model subspace and the data subspace. The dipolar orientations, a form of "diverse polarization", are easily extracted using the associated principal vectors.
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