Signal-space projection method for separating MEG or EEG into components
Signal-space projection method for separating MEG or EEG into components
复制标题
DOI：
10.1007/bf02534144
复制
发表时间：
1997-03-01
影响因子：
3.2
通讯作者：
Ilmoniemi, RJ
中科院分区：
文献类型：
--
作者：
Uusitalo, MA;Ilmoniemi, RJ
通过测量外部多个位置的磁场和/或电场，然后构造反问题的解，即确定可能产生测量场的电流配置，可以估计导电物体内的电流。不幸的是，这个问题没有唯一的解决方案(Helmholtz，1853)，除非做出限制性的假设。最小范数估计(Ham/~.L，Inen和ILMONIEMI，1994)给出了一种在已知震源分布的最小先验信息的情况下，期望总体误差最小的解。已经研究了其他方法来估计产生测量信号的连续电流分布(Pascual-Marqui等人，1994；Wang等人，1995；GORODNITSKY等人，1995)。一种不同的方法是将大脑活动分为不同的组成部分，如电流偶极子(ScHERG，1990；Mosher等人，1992)。在这里，我们将这种方法扩展到任意的当前配置。在我们的信号空间投影(SSP)方法中，d个传感器测量的信号被认为是在d维信号空间中形成的时变向量。分量向量，即由不同神经元来源产生的信号，在信号空间中具有不同的固定方向。换句话说，每个源都有一个独特而稳定的场模式。产生相同测量场图案的所有当前电场都不能基于场来区分：它们在信号空间中具有相同的矢量方向，因此属于电流配置的相同等价类(Tesche等人，1995A)。信号空间中代表不同等价类的矢量之间的夹角，例如分量矢量之间的夹角，是信号空间中等价类的相似性的度量，也是表征源的可分离性的一种方法。这个角度的余弦以前被用作地形分布之间差异的数字特征(Desmedt和Chalk[。如果形成测量的多通道信号的至少一个分量向量的方向可以从数据中确定，或者是已知的，则可以使用SSP来简化后续分析。例如，如果诱发反应中的早期偏转是由一个源产生的，而响应的其余部分是来自该源和其他源的信号的混合，则SSP可以将数据分成两个部分，使得早期源只对一个部分起作用。一般地，信号被分为两个正交部分：S~，包括信号空间方向已知源的时变贡献；S~，包括其余信号。然后，SL~和S J_2都可以分别进行更详细的分析。通过分析st，我们可以发现最初被S掩盖的活动。另一方面，STL中包含的信源具有增强的信噪比。通过对选定的皮质斑块中的源进行正演模拟，可以形成一个空间滤波器，该空间过滤器仅选择性地通过可能已经由给定斑块中的电流产生的信号。如果可以确定由伪像定义的子空间，则可以分析伪像SL。
CURRENTS INSIDE a conducting body can be estimated by measuring the magnetic and/or the electric field at multiple locations outside and then constructing a solution to the inverse problem, ie determining a current configuration that could have produced the measured field. Unfortunately, there is no unique solution to this problem (HELMHOLTZ, 1853) unless restricting assumptions are made. The minimum-norm estimate (HAM/~. L,~ INEN and ILMONIEMI, 1994) provides a solution with the smallest expected overall error when minimum a priori information about the source distribution is available. Other methods to estimate a continuous current distribution producing the measured signals have been studied (PASCUAL-MARQUI et al., 1994; WANG et aL, 1995; GORODNITSKY, et al., 1995). A different approach is to divide the brain activity into discrete components such as current dipoles (ScHERG, 1990; MOSHER et al., 1992). Here we widen this approach into arbitrary current configurations. In our signal-space projection (SSP) method, the signals measured by d sensors are considered to form a time-varying vector in a d-dimensional signal space. The component vectors,, ie the signals caused by the different neuronal sources, have different and fixed orientations in the signal space. In other words, each source has a distinct and stable field pattern. All the current eonfi~ marations producing the same measured field pattern are indistinguishable on the basis of the field: they have the same vector direction in the signal space and thus belong to the same equivalence class of current configurations (TESCHE et al., 1995a). The angle in the signal space between vectors representing different equivalence classes, eg between component vectors, is a measure of similarity of the equivalence classes in signal space and a way to characterise the separability of sources. The cosine of this angle has previously been used as a numerical charaeterisation of the difference between topographical distributions (DESMEDT and CHALK [. IN, 1989).If the direction of at least one of the component vectors forming the measured multi-channel signal can be determined from the data, or is known otherwise, SSP can be used to simplify subsequent analysis. For example, if an early deflection in an evoked response is produced by one source, and the rest of the response is a mixture of signals from this and other sources, SSP can separate the data into two parts so that the early source contributes only to one part. In general, the signals are divided into two orthogonal parts: s~, including the time-varying contribution from sources with known signalspace directions; and s~ _, including the rest of the signals. Both sl~ and s j_ can then be analysed separately in more detail. By analysing st, we can detect activity originally masked by s~. On the other hand, the sources included in stl are seen with an enhanced signal-to-noise ratio. By forward modelling of sources in selected patches of cortex, it is possible to form a spatial filter that selectively passes only the signals that may have been generated by currents in the given patches. If the subspace defined by artefacts can be determined, the artefactflee SL can be analysed.