Linear transformations of data space in MEG
Linear transformations of data space in MEG
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DOI：
10.1088/0031-9155/44/8/317
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发表时间：
1999-08-01
影响因子：
3.5
通讯作者：
Ioannides, AA
中科院分区：
文献类型：
--
作者：
Gross, J;Ioannides, AA
脑磁图（MEG）是一种无创测量脑内离子电流产生的微小磁场的方法。由于传感器的复杂灵敏度曲线，测量数据是电流的非平凡表示，其中本地发电机特有的信息分布在许多通道上，并且每个通道包含来自许多此类发电机的贡献的混合。我们提出了一个框架，该框架通过线性变换生成数据的新表示，该线性变换的设计使得在一个或多个新的虚拟通道中优化某些所需的属性。第一品质因数的建议来描述之间的关系的测量数据和底层电流。在这种情况下，建立新的框架，首先显示如何转换矩阵本身的设计，然后由它的应用程序到真实的和模拟数据。结果表明，所提出的数据空间的线性变换提供了一个计算效率高的工具，用于分析和非常需要的降维的数据。
Magnetoencephalography (MEG) is a method which allows the non-invasive measurement of the minute magnetic field which is generated by ion currents in the brain. Due to the complex sensitivity profile of the sensors, the measured data are a non-trivial representation of the currents where information specific to local generators is distributed across many channels and each channel contains a mixture of contributions from many such generators. We propose a framework which generates a new representation of the data through a linear transformation which is designed so that some desired property is optimized in one or more new virtual channel(s). First figures of merit are suggested to describe the relation between the measured data and the underlying currents. Within this context the new framework is established by first showing how the transformation matrix itself is designed and then by its application to real and simulated data. The results demonstrate that the proposed linear transformations of data space provide a computationally efficient tool for analysis and a very much needed dimensional reduction of the data.