SURROGATE DATA-ANALYSIS OF SLEEP ELECTROENCEPHALOGRAMS REVEALS EVIDENCE FOR NONLINEARITY

We tested the hypothesis of whether sleep electroencephalographic (EEG ) signals of different time windows (164 s, 82 s, 41 s and 20.5 s) are in accordance with linear stochastic models. For this purpose we anal yzed the all-night sleep electroencephalogram of a healthy subject and corresponding Gaussian-rescaled phase randomized surrogates with a ba ttery of five nonlinear measures. The following nonlinear measures wer e implemented: largest Lyapunov exponent L1, correlation dimension D2, and the Green-Savit measures delta 2, delta 4 and delta 6. The hypoth esis of linear stochastic data was rejected with high statistical Sign ificance. L1 and D2 yielded the most pronounced effects, while the Gre en-Savit measures were only partially successful in differentiating EE G epochs from the phase randomized surrogates. For L1 and D2 the effic iency of distinguishing EEG signals from linear stochastic data decrea sed with shortening of the time window. Altogether, our results indica te that EEG signals exhibit nonlinear elements and cannot completely b e described by linear stochastic models.