Decomposition of magnetoencephalographic data into components corresponding to deep and superficial sources.
Decomposition of magnetoencephalographic data into components corresponding to deep and superficial sources.
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将脑磁图数据分解为对应于深层和浅层源的分量。
DOI：
10.1109/tbme.2008.919120
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发表时间：
2008
期刊：
影响因子：
--
通讯作者：
Sclabassi,RobertJ
中科院分区：
文献类型：
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作者：
Ozkurt,TolgaEsat;Sun,Mingui;Sclabassi,RobertJ
我们扩展了信号空间分离（SSS）的方法来分解多通道脑磁图（MEG）数据到感兴趣的区域内的头部。它已被证明，SSS方法可以转换成由神经生物学源产生的信号分量和外部源产生的噪声分量的头部以外的MEG数据。在本文中，我们表明，通过SSS方法获得的信号分量可以进一步分解成一个简单的操作，从大脑内的深层和浅层源的信号。这是通过使用一个方案，利用波束空间的方法，依赖于一个线性变换，最大化的源空间的功率的兴趣。仿真和真实的脑磁图数据的实验结果表明了该算法的有效性和准确性。
We extend the signal space separation (SSS) method to decompose multichannel magnetoencephalographic (MEG) data into regions of interest inside the head. It has been shown that the SSS method can transform MEG data into a signal component generated by neurobiological sources and a noise component generated by external sources outside the head. In this paper, we show that the signal component obtained by the SSS method can be further decomposed by a simple operation into signals originating from deep and superficial sources within the brain. This is achieved by using a scheme that exploits the beamspace methodology that relies on a linear transformation that maximizes the power of the source space of interest. The efficiency and accuracy of the algorithm are demonstrated by experiments utilizing both simulated and real MEG data.