Abstracts

RATIONALE: Nonlinear time series analyses provide increasing evidence for nonlinearity in EEG time series of epilepsy patients, rendering additional information about the spatio-temporal dynamics of the epileptogenic process. Most analysis techniques require stationarity of the time series under investigation and it is often objected that the inherent nonstationary character of brain electrical activity might result in spurious detection of nonlinearity. To examine this objection, we quantify different degrees of nonstationarity and relate our findings to tests for nonlinearity. METHODS: 1000 EEG segments recorded intracranially in patients with focal epilepsies and covering different interictal, ictal, pre- and postictal states were analyzed. To quantify nonstationarity and to describe dynamical changes in the EEG we used the recurrence of points in the reconstructed state space. EEG time series were tested for evidence of nonlinearity by combining different nonlinear measures with surrogate data tests. RESULTS: EEG segments from all states exhibited nonlinearity for all degrees of nonstationarity including approximate stationarity. On the other hand, linearity could also be found in time series covering all scales of nonstationarity. CONCLUSIONS: Since no general relationship could be established between nonstationarity and nonlinearity, the aforementioned objection could clearly be rejected. Moreover, we hypothesize that characteristic time scales of nonstationarity in brain electric activity might even contribute to an improved understanding of the epileptogenic process. [Supported by the Deutsche Forschungsgemeinschaft]