EEG analysis with nonlinear deterministic and stochastic methods: a combined strategy

We describe nonlinear deterministic versus stochastic methodology, their ap plications to EEG research and the neurophysiological background underlying both approaches. Nonlinear methods are based on the concept of attractors in phase space. This concept on the one hand incorporates the idea of an au tonomous (stationary) system, on the other hand implicates the investigatio n of a long time evolution. It is an unresolved problem in nonlinear EEG re search that nonlinear methods per se give no feedback about the stationarit y aspect. Hence, we introduce a combined strategy utilizing both stochastic and nonlinear deterministic methods. We propose, in a first step to segmen t the EEG time series into piecewise quasi-stationary epochs by means of no nparametric change point analysis. Subsequently, nonlinear measures can be estimated with higher confidence for the segmented epochs fullfilling the s tationarity condition.