Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state

TLDR

The study compares dynamical properties of brain electrical activity across recording regions and physiological versus pathological brain states. The authors analyze EEG time series using nonlinear prediction error, effective correlation dimension, and iterative amplitude‑adjusted surrogate data, applying a weak‑stationarity criterion to surface recordings (eyes closed/open) and intracranial recordings from epilepsy patients during seizure‑free intervals and seizures. They find that surface EEG with eyes open conforms to a Gaussian linear stochastic process, seizure activity shows the strongest nonlinear deterministic dynamics, and other recordings fall between these extremes.

Abstract

We compare dynamical properties of brain electrical activity from different recording regions and from different physiological and pathological brain states. Using the nonlinear prediction error and an estimate of an effective correlation dimension in combination with the method of iterative amplitude adjusted surrogate data, we analyze sets of electroencephalographic (EEG) time series: surface EEG recordings from healthy volunteers with eyes closed and eyes open, and intracranial EEG recordings from epilepsy patients during the seizure free interval from within and from outside the seizure generating area as well as intracranial EEG recordings of epileptic seizures. As a preanalysis step an inclusion criterion of weak stationarity was applied. Surface EEG recordings with eyes open were compatible with the surrogates' null hypothesis of a Gaussian linear stochastic process. Strongest indications of nonlinear deterministic dynamics were found for seizure activity. Results of the other sets were found to be inbetween these two extremes.