Contrasting Electroencephalography-Derived Entropy and Neural Oscillations With Highly Skilled Meditators

Abstract

Meditation is an umbrella term for a number of mental training practices designed to improve the monitoring and regulation of attention and emotion. Some forms of meditation are now being used for clinical intervention. To accompany the increased clinical interest in meditation, research investigating the neural basis of these practices is needed. A central hypothesis of contemplative neuroscience is that meditative states, which are unique on a phenomenological level, differ on a neurophysiological level. To identify the electrophysiological correlates of meditation practice, the electrical brain activity of highly skilled meditators engaging in one of six meditation styles (shamatha, vipassana, zazen, dzogchen, tonglen, and visualization) was recorded. A mind-wandering task served as a control. Lempel–Ziv complexity showed differences in nonlinear brain dynamics (entropy) during meditation compared with mind wandering, suggesting that meditation, regardless of practice, affects neural complexity. In contrast, there were no differences in power spectra at six different frequency bands, likely due to the fact that participants engaged in different meditation practices. Finally, exploratory analyses suggest neurological differences among meditation practices. These findings highlight the importance of studying the electroencephalography (EEG) correlates of different meditative practices.

Nonlinear EEG signatures of mind wandering during breath focus meditation

“…Our results are difficult to compare with previous studies that assessed EEG complexity during meditation (Aftanas and Golocheikine, 2002;Huang and Lo, 2009;Kakumanu et al, 2018;Kumar et al, 2020;Martinez Vivot et al, 2020;Young et al, 2021) due to differences in the meditation tradition, the level of expertise and the adopted complexity metric. If we take into account all these factors, our results are comparable and in line with a recent study (Kakumanu et al, 2018).…”

“…To our knowledge, no other study investigated the exact same metrics in the same population while participants adopted the same meditation technique. Nonetheless, it is important to underline that a previous study showed the opposite pattern of results (decreased complexity during meditation relative to mind wandering) using the LZC metric in experienced meditators (Young et al, 2021). Although this latter study investigated different meditation techniques, their results could indicate that, as pointed by a recent study (Rodriguez-Larios et al, 2021), the EEG correlates of mind wandering and meditative states differ between experienced meditators and novices.…”

“…EEG complexity metrics have been used to characterize mind wandering and meditative states in a few studies (Martinez Vivot et al, 2020; Young et al, 2021). However, these studies were carried out in experienced meditators only.…”

Nonlinear EEG signatures of mind wandering during breath focus meditation

“…However, this is not fully consistent with previous literature with experienced meditators. Although some studies have indeed associated meditative states with higher EEG complexity ( Kakumanu et al, 2018 ; Vivot et al, 2020 ), other studies have reported the opposite effect ( Aftanas and Golocheikine, 2002 ; Young et al, 2021 ). It is possible that inconsistencies regarding the relationship between EEG complexity and meditative states are due to differences in the meditation tradition, the level of expertise and the adopted complexity metric ( Aftanas and Golocheikine, 2002 ; Huang and Lo, 2009 ; Kakumanu et al, 2018 ; Kumar et al, 2020 ; Vivot et al, 2020 ; Young et al, 2021 ).…”

“…Although some studies have indeed associated meditative states with higher EEG complexity ( Kakumanu et al, 2018 ; Vivot et al, 2020 ), other studies have reported the opposite effect ( Aftanas and Golocheikine, 2002 ; Young et al, 2021 ). It is possible that inconsistencies regarding the relationship between EEG complexity and meditative states are due to differences in the meditation tradition, the level of expertise and the adopted complexity metric ( Aftanas and Golocheikine, 2002 ; Huang and Lo, 2009 ; Kakumanu et al, 2018 ; Kumar et al, 2020 ; Vivot et al, 2020 ; Young et al, 2021 ). Given the great number of possible EEG metrics/traditions/levels of expertise that can be assessed, the only way of achieving a consensus in this field would be to make raw EEG data from different studies publicly available thereby allowing to assess these factors systematically.…”