Analysis of nonlinearity in normal and epileptic EEG signals

Abstract

The nonlinearity in normal and epileptic electroencephalogram (EEG) signals is investigated in this paper by the delay vector variance (DVV) method, which determines the degree of nonlinearity of the tested time series by comparing the target variances of the tested time series to those of the corresponding surrogate time series. The results of numerical experiments show that both normal and epileptic EEG signals are of nonlinearity, whereas epileptic EEG signals are of higher degree of nonlinearity than normal EEG signals. Based on this, it is proposed that degree of nonlinearity could provide useful information for epileptic seizure characterization. Moreover, the degree of nonlinearity of epileptic EEG time series fluctuates more briskly than that of normal EEG time series.