Nonlinear Recurrent Dynamics and Long-Term Nonstationarities in EEG Alpha Cortical Activity: Implications for Choosing Adequate Segment Length in Nonlinear EEG Analyses

Abstract

Nonlinear analysis of EEG recordings allows detection of characteristics that would probably be neglected by linear methods. This study aimed to determine a suitable epoch length for nonlinear analysis of EEG data based on its recurrence rate in EEG alpha activity (electrodes Fz, Oz, and Pz) from 28 healthy and 64 major depressive disorder subjects. Two nonlinear metrics, Lempel-Ziv complexity and scaling index, were applied in sliding windows of 20 seconds shifted every 1 second and in nonoverlapping windows of 1 minute. In addition, linear spectral analysis was carried out for comparison with the nonlinear results. The analysis with sliding windows showed that the cortical dynamics underlying alpha activity had a recurrence period of around 40 seconds in both groups. In the analysis with nonoverlapping windows, long-term nonstationarities entailed changes over time in the nonlinear dynamics that became significantly different between epochs across time, which was not detected with the linear spectral analysis. Findings suggest that epoch lengths shorter than 40 seconds neglect information in EEG nonlinear studies. In turn, linear analysis did not detect characteristics from long-term nonstationarities in EEG alpha waves of control subjects and patients with major depressive disorder patients. We recommend that application of nonlinear metrics in EEG time series, particularly of alpha activity, should be carried out with epochs around 60 seconds. In addition, this study aimed to demonstrate that long-term nonlinearities are inherent to the cortical brain dynamics regardless of the presence or absence of a mental disorder.

Study on the Effect of Judgment Excitation Mode to Relieve Driving Fatigue Based on MF-DFA

“…EEG, which can reflect the characteristics of human brain activity, is one of the most important human physiological signals and is considered to be the most reliable indicator of a fatigued driving state. As a nonlinear dynamical time series [ 14 ], EEG is studied using some traditional linear methods that lose many of the inherent properties of a scale-free (self-affine) dynamical signal [ 15 ]. For example, the method based on power spectrum is a poor descriptor of the local irregularity of a signal; the method based on Fourier transform can not describe the basic information of neuron dynamics of non-periodic and non-stationary signals; the method based on brain network requires a large number of redundant channels of leads, which have large noise interference and high computational complexity.…”