OBJECTIVES: 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 series 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. 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 21 channel EEG data recorded during 3 generalized tonic-clonic seizures induced in an individual as part of a course of electroconvulsive therapy for major depression. RESULTS: This technique identified EEG patterns consistent with prior observations of generalized tonic-clonic EEG data. In addition, it quantified a decrease in dominant frequency content over the seizures and suggested for the first time that this decrease is continuous across the end of the seizures. The analysis also identified the possibility that low-frequency components may decrease in dominant frequency sooner and to a greater extent in more therapeutically effective ECT seizures. CONCLUSIONS: Eigenanalysis of time-varying autoregressive models has promise for improving the analysis of EEG time series.

Andrew Krystal is Assistant Professor in the Department of Psychiatry and Behavioral Sciences and Director of the Quantitative EEG Laboratory at Duke University Medical Center, Durham NC 27710, USA. Raquel Prado is Assistant Professor, Departamento de Cómputo Científico y Estadística, Universidad Simón Bolívar, Caracas, Venezuela. Mike West is Professor and Director in the Institute of Statistics and Decision Sciences, Duke University, Durham, NC 27708-0251, USA. The authors acknowledge partial financial support under grants NIMH K20MH01151 and NSF/DMS-9704432.

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