Surrogate Data Method Requires End-Matched Segmentation of Electroencephalographic Signals to Estimate Non-linearity - Päeske, Laura; Bachmann, Maie; Põld, Toomas; de Oliveira, Sara Pereira Mendes; Lass, Jaanus; Raik, Jaan; Hinrikus, Hiie (Frontiers in Physiology, 2018)
The aim of the study is to clarify the impact of the strong cyclic signal component on the results of surrogate data method in the case of resting electroencephalographic (EEG) signals. In addition, the impact of segment length is analyzed. Different nonlinear measures (fractality, complexity et al.) of neural signals have been demonstrated to be useful to infer the nonlinearity of brain functioning from EEG. The surrogate data method is often applied to test whether or not the nonlinear structure can be captured from the data. In addition, a growing number of studies are using surrogate data method to determine the statistical threshold of connectivity values in network analysis. Current study focuses on the conventional segmentation of EEG signals, which could lead to false results of surrogate data method. More specifically, the necessity to use end-matched segments that contain an integer number of dominant frequency periods is studied. EEG recordings from 80 healthy volunteers during eyes-closed resting state were analyzed using multivariate surrogate data method. The artiﬁcial surrogate data were generated by shufﬂing the phase spectra of original signals. The null hypothesis that time series were generated by a linear process was rejected by statistically comparing the nonlinear statistics calculated for original and surrogate data sets. Five discriminating statistics were used as nonlinear estimators: Higuchi fractal dimension (HFD), Katz fractal dimension (KFD), Lempel-Ziv complexity (LZC), sample entropy (SampEn) and synchronization likelihood (SL). The results indicate that the number of segments evaluated as nonlinear differs in the case of various nonlinear measures and changes with the segment length. The main conclusion is that the dependence on the deviation of the segment length from full periods of dominant EEG frequency has nonmonotonic character and causes misleading results in the evaluation of nonlinearity. Therefore, in the case of the signals with nonmonotonic spectrum and strong dominant frequency, the correct use of surrogate data method requires the signal length comprising of full periods of the spectrum dominant frequency. The study is important to understand the influence of incorrect selection of EEG signal segment length for surrogate data method to estimate nonlinearity.