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.