Non-Linear EEG Measures in Meditation

Abstract

In this study, the performance of Sevcik's algorithm that calculates the fractal dimension and permutation entropy as discriminants to detect calming and insight meditation in electroencephalographic (EEG) signals was assessed. The proposed methods were applied to EEG recordings from meditators practicing insight meditation and calming meditation before as well as during both types of meditation. Analysis was conducted using statistical hypothesis testing to determine the validity of the proposed meditation-identifying techniques. For both types of meditation, there was a statistically significant reduction in the permutation entropy. This result can be explained by the increased EEG synchronization, which is repeatedly observed in the course of meditation. In contrast, the fractal dimension (FD) was significantly increased during calming meditation, but during insight meditation, no statistically significant change was detected. Increased FD during meditation can be interpreted as an increase in self-similarity of EEG signals during self-organisation of hierarchical structure oscillators in the brain. Our results indicate that fractal dimension and permutation entropy could be used as parameters to detect both types of meditation. The permutation entropy is advantageous compared with the fractal dimension because it does not require a stationary signal.

Characterization of EEG patterns in brain-injured subjects and controls after a Snoezelen® intervention

“…The patterns of changes observed using MF, IAF, SampEn and LZC parameters did not necessarily imply that Snoezelen ® 15 therapy produces neural repair in participants. However, we hypothesize that the changes described in this study are related with higher levels of psychophysiological relaxation of the participants, since similar EEG alterations have been described during different relaxation strategies [35][36][37][38][39][40][41]. Nevertheless, other results (e.g.…”

“…When compared to rest, meditation seems to be accompanied by a complexity decrease, maybe due to the disconnection of irrelevant brain networks for the maintenance of focused internalized attention and inhibition of inappropriate information [39]. In a recent study, a statistically significant reduction in the permutation entropy was found after insight meditation and calming meditation [41]. Natarajan et al [40] demonstrated that EEG is characterized by a less complex and 16 more regular behavior after music and reflexological stimulation, which completely agrees with our SampEn and LZC results.…”

“…This neurophysiologic pattern (increase in theta activity) agrees with our IAF results. Nonlinear studies, although less common, have been also applied to characterize relaxation patterns [39][40][41]. When compared to rest, meditation seems to be accompanied by a complexity decrease, maybe due to the disconnection of irrelevant brain networks for the maintenance of focused internalized attention and inhibition of inappropriate information [39].…”

Mapping meditative states and stages with electrophysiology: concepts, classifications, and methods

“…Nonlinear statistics, such as Lyapunov stability theory and stochastic analysis (Wang et al, 2018), and nonlinear interdependence to reflect dynamical systems complexity (Lo et al, 2018), are being embraced to investigate the poly-dimensionality and nonlinearity of meditation EEG-substrates. Fractal dimension/FD (the degree of chaos within the brain's time series) and permutation entropy (the degree of uncertainty within a random variable, such as EEG signal) may provide statistical sensitivity to detect meditation-associated neural correlates, since increased FD (network complexity) has been observed in experienced Vipassana meditators (Kakumanu et al, 2018), and "focused breath" vs. "open monitoring" meditation, significantly so for the former (Vyšata et al, 2014). Moreover, permutation entropy may have utility in gauging meditation stability, since reduced PE related to either meditative-state and/or proficiency was reported in both studies.…”

Consumer grade EEG devices: are they usable for control tasks?

“…There has been a great deal of research works focusing on detecting the attention and relaxation (meditation) states of mind from the characteristics of EEG ( Aftanas & Golocheikine, 2001 ; Jacobs & Friedman, 2004 ; Lin & John, 2006 ; Hamadicharef et al, 2009 ; Rebolledo-Mendez et al, 2009 ; Jiang et al, 2011 ; Li et al, 2011 ; Liu, Chiang & Chu, 2013 ; Patsis et al, 2013 ; Vyšata et al, 2014 ; Kaur & Singh, 2015 ). The detection of the attention and meditation is important in many fields, including clinical studies of stress reduction, sleep deprivation, fatigue, educational studies of learner attention and game studies of player concentration and engagement.…”