Measurement of Spectral Properties for a Non-Gaussian Process Model of EEG

The electrical activity of the brain produces a signal which in general is a non-stationary process. Nevertheless stationary Gaussian models are used successfully to characterize the signal in short sections without obvious non-stationarities. However, it has been observed that the characterization of the signal differs more between separate sections than may be explained using a stationary Gaussian model. In this paper a stationary but non-Gaussian process model of EEG is developed and analyzed. It is shown that for a given measurement time estimates of the autocorrelation function and the power spectrum are less accurate for this process than for the corresponding process in a Gaussian model. Upper limits for the increase in the variance of the estimates are given. The process has been simulated on a digital computer and the experimental results verify the theoretical properties.