Kaipio J P, Karjalainen P A
Department of Applied Physics, University of Kuopio, Finland.
Biol Cybern. 1997 May;76(5):349-56. doi: 10.1007/s004220050348.
In this paper we present a systematic method for generating simulations of nonstationary EEG. Such simulations are needed, for example, in the evaluation of tracking algorithms. First a state evolution process is simulated. The states are initially represented as segments of stationary autoregressive processes which are described with the corresponding predictor coefficients and prediction error variances. These parameters are then concatenated to give a piecewise time-invariant parameter evolution. The evolution is projected onto an appropriately selected set of smoothly time-varying functions. This projection is used to generate the final EEG simulation. As an example we use this method to simulate the EEG of a drowsy rat. This EEG can be described as toggling between two states that differ in the degree of synchronization of the activity-inducing neuron clusters.
在本文中，我们提出了一种用于生成非平稳脑电图模拟的系统方法。例如，在跟踪算法评估中就需要这样的模拟。首先模拟一个状态演化过程。状态最初表示为平稳自回归过程的片段，这些片段用相应的预测系数和预测误差方差来描述。然后将这些参数连接起来，得到一个分段时不变的参数演化。将该演化投影到一组适当选择的平滑时变函数上。此投影用于生成最终的脑电图模拟。作为一个例子，我们使用这种方法来模拟一只困倦大鼠的脑电图。这种脑电图可以描述为在两个状态之间切换，这两个状态在诱发活动的神经元簇的同步程度上有所不同。
