Department of Neurophysiology, Nencki Institute of Experimental Biology, 02-093 Warsaw, Poland.
Neural Comput. 2010 Jan;22(1):48-60. doi: 10.1162/neco.2009.07-08-831.
We propose two ways of estimating current source density (CSD) from measurements of voltage on a Cartesian grid with missing recording points using the inverse CSD method. The simplest approach is to substitute local averages (LA) in place of missing data. A more elaborate alternative is to estimate a smaller number of CSD parameters than the actual number of recordings and to take the least-squares fit (LS). We compare the two approaches in the three-dimensional case on several sets of surrogate and experimental data, for varying numbers of missing data points, and discuss their advantages and drawbacks. One can construct CSD distributions for which one or the other approach is better. However, in general, the LA method is to be recommended as being more stable and more robust to variations in the recorded fields.
我们提出了两种从笛卡尔网格上的电压测量中估计电流源密度（CSD）的方法，这些测量中存在缺失的记录点，并使用逆 CSD 方法。最简单的方法是用局部平均值（LA）代替缺失数据。更复杂的替代方法是估计比实际记录数量更少的 CSD 参数，并进行最小二乘拟合（LS）。我们在几种替代数据和实验数据上比较了这两种方法在三维情况下的表现，对于不同数量的缺失数据点，并讨论了它们的优缺点。对于某些 CSD 分布，一种方法可能比另一种方法更好。然而，一般来说，LA 方法更稳定，对记录场的变化更鲁棒。
