There has been an increasing trend towards the use of complexity analysis in quantifying
neural activity measured by electroencephalography (EEG) signals. On top of revealing …

Background and objective For more than 20 years, Higuchi's fractal dimension (HFD), as a
nonlinear method, has occupied an important place in the analysis of biological signals. The …

Recent findings identified electroencephalography (EEG) microstates as the
electrophysiological correlates of fMRI resting-state networks. Microstates are defined as …

Multiscale entropy (MSE) analysis is a novel entropy-based approach for measuring
dynamical complexity in physiological systems over a range of temporal scales. To evaluate …

A method is presented for investigation of EEG of children with autistic spectrum disorder
using complexity and chaos theory with the goal of discovering a nonlinear feature space …

Recently, the visibility graph (VG) algorithm was proposed for mapping a time series to a
graph to study complexity and fractality of the time series through investigation of the …

Recently the senior author and his associates developed a spatiotemporal wavelet-chaos
methodology for the analysis of electroencephalograms (EEGs) and their subbands for …

Recent reports of functional and anatomical studies have provided evidence that aberrant
neural connectivity lies at the heart of many mental disorders. Information related to neural …

Neural complexity and brain entropy (BEN) have gained greater interest in recent years. The
dynamics of neural signals and their relations with information processing continue to be …

In this chapter, empirical wavelet transformation is used to decompose the highly
nonstationary electroencephalogram signals into modes in a Fourier spectrum. Linear and …