OBJECTIVE: Those who analyze EEG data require quantitative techniques that can be validly applied to time series exhibiting ranges of non-stationary behavior. Our objective is to introduce a new analysis technique based on formal non-stationary time series models. This novel method provides a decomposition of the time series into a set of "latent" components with time-varying frequency content. The identification of these components can lead to practical insights and quantitative comparisons of changes in frequency structure over time in EEG time series. DESIGN and METHODS: The technique begins with the development of time-varying autoregressive models of the EEG time series. Such models have been previously used in EEG analysis but we extend their utility by the introduction of eigenstructure decomposition methods. We review the basis and implementation of this method and report on the analysis of 2 channel EEG data recorded during 3 generalized tonic-clonic seizures ind...