SIMULATION OF NONSTATIONARY EEG

In this paper we present a systematic method for generating simulation s of nonstationary EEG. Such simulations are needed, for example, in t he evaluation of tracking algorithms. First a state evolution process is simulated. The states are initially represented as segments of stat ionary autoregressive processes which are described with the correspon ding predictor coefficients and prediction error variances. These para meters are then concatenated to give a piecewise time-invariant parame ter evolution. The evolution is projected onto an appropriately select ed 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 toggli ng between two states that differ in the degree of synchronization of the activity-inducing neuron clusters.