Depth of Meditation is Associated with Different Levels of Brain Electrical Activity

Depth of Meditation is Associated with Different Levels of Brain Electrical Activity

 

By John M. de Castro, Ph.D.

 

“Neuroscientific studies, particularly EEG, are revealing much about the neural correlates of meditation in the hopes of understanding why it has therapeutic value, and as a way to probe the nature of self and consciousness.” – Aaron Nitzkin

 

Meditation training has been shown to improve health and well-being. It has also been found to be effective for a large array of medical and psychiatric conditions, either stand-alone or in combination with more traditional therapies. There are a number of ways that meditation practices produce these benefits, including changes to the brain and physiology. One way to observe the effects of meditation on neural activity is to measure changes in the electroencephalogram (EEG), the rhythmic electrical activity that can be recorded from the scalp.

 

The recorded activity can be separated into frequency bands. Delta activity consists of oscillations in the 0.5-3 cycles per second band. Theta activity in the EEG consists of oscillations in the 4-8 cycles per second band. Alpha activity consists of oscillations in the 8-12 cycles per second band. Beta activity consists of oscillations in the 15-25 cycles per second band while Gamma activity occurs in the 35-45 cycles per second band. Changes in these brain activities can be compared during different depths of meditation.

 

In today’s Research News article “Alpha and theta oscillations are inversely related to progressive levels of meditation depth.” (See summary below or view the full text of the study at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8633885/ ) Katyal and Goldin recruited healthy adult participants who were long-term meditators and demographically matched meditation naïve participants. They had their electroencephalogram (EEG) recorded during 4 blocks of either listening to a story, listening to music, or 2 6-minute blocks of meditation. They self-reported their depth of meditation after each block.

 

They found, not surprisingly, that in comparison to the meditation naïve participants, the experienced meditators had significantly greater depth of meditation. They also found that as the depth of meditation increased the alpha rhythm in the EEG significantly increased while the theta rhythm significantly decreased. This was true for both groups.

 

The alpha rhythm has been associated with relaxation and a suppression of mind wandering and distraction. Similarly, the greater the depth of meditation the less distraction and mind wandering. Thus, the increase in the alpha rhythm with increasing depth of meditation is reasonable and completely predictable. The theta rhythm is associated with dreaminess and sleep. That the theta rhythm is lowest with higher depths of meditation makes sense as depth is associated with alert awareness. Hence, the brain wave patterns seen during meditation are reflective of the depth of meditation.

 

So, depth of meditation is associated with different levels of brain electrical activity

 

neurocognitive mechanisms that are present during both self-generated thought and controlled cognitive processes (i.e. the integration between the memory and executive components of cognition via alpha:theta cross-frequency coupling) are minimized during meditative practices.” – Julio Rodriguez-Larios

 

CMCS – Center for Mindfulness and Contemplative Studies

 

This and other Contemplative Studies posts are available on Twitter @MindfulResearch

 

Study Summary

 

Katyal, S., & Goldin, P. (2021). Alpha and theta oscillations are inversely related to progressive levels of meditation depth. Neuroscience of consciousness, 2021(1), niab042. https://doi.org/10.1093/nc/niab042

 

Highlights

  • Our study reveals neurophysiological changes that occur as meditation experiences become deeper.
  • Alpha and theta brainwaves are two reliable neurophysiological signatures of meditation.
  • Theta activity increased with more distractions and was suppressed during deeper experiences.
  • Increased alpha activity was related to fewer distractions and more deeper meditation experiences.
  • Deeper meditation experiences appear to involve a suppression of executive neural processing.

Abstract

Meditation training is proposed to enhance mental well-being by modulating neural activity, particularly alpha and theta brain oscillations, and autonomic activity. Although such enhancement also depends on the quality of meditation, little is known about how these neural and physiological changes relate to meditation quality. One model characterizes meditation quality as five increasing levels of ‘depth’: hindrances, relaxation, concentration, transpersonal qualities and nonduality. We investigated the neural oscillatory (theta, alpha, beta and gamma) and physiological (respiration rate, heart rate and heart rate variability) correlates of the self-reported meditation depth in long-term meditators (LTMs) and meditation-naïve controls (CTLs). To determine the neural and physiological correlates of meditation depth, we modelled the change in the slope of the relationship between self-reported experiential degree at each of the five depth levels and the multiple neural and physiological measures. CTLs reported experiencing more ‘hindrances’ than LTMs, while LTMs reported more ‘transpersonal qualities’ and ‘nonduality’ compared to CTLs, confirming the experiential manipulation of meditation depth. We found that in both groups, theta (4–6 Hz) and alpha (7–13 Hz) oscillations were related to meditation depth in a precisely opposite manner. The theta amplitude positively correlated with ‘hindrances’ and increasingly negatively correlated with increasing meditation depth levels. Alpha amplitude negatively correlated with ‘hindrances’ and increasingly positively with increasing depth levels. The increase in the inverse association between theta and meditation depth occurred over different scalp locations in the two groups—frontal midline in LTMs and frontal lateral in CTLs—possibly reflecting the downregulation of two different aspects of executive processing—monitoring and attention regulation, respectively—during deep meditation. These results suggest a functional dissociation of the two classical neural signatures of meditation training, namely, alpha and theta oscillations. Moreover, while essential for overcoming ‘hindrances’, executive neural processing appears to be downregulated during deeper meditation experiences.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8633885/

 

Improve the Brains Ability to Directly Control Computers with Mindfulness

Improve the Brains Ability to Directly Control Computers with Mindfulness

 

By John M. de Castro, Ph.D.

 

“Meditation has been widely practiced for well-being and improving health,” said He. Our work demonstrates that it can also enhance a person’s mental power for mind control, and may facilitate broad use of noninvasive brain-computer interface technology.” – Bin He

 

It has long been a dream to develop methods to allow the brain to directly alter external devices. The efforts have been focused on developing a brain-computer interface such that recorded electrical activity of the brain is interfaced with a computer allowing control of the computer by the activity. It is hypothesized that a brain computer interface might be able to provide an alternative method to control muscles in patients with severe neuromuscular diseases.

 

Brain-computer interface methods have been developed but suffer from long training times before the participant is capable of affecting the computer activity. Meditation has been shown to alter the activity of the brain. Meditation training may make the individual better at controlling their brain activity. Hence, an interesting research question is to investigate whether meditation training will improve the ability to learn to control a computer with the brain’s electrical activity.

 

In today’s Research News article “Frontolimbic alpha activity tracks intentional rest BCI control improvement through mindfulness meditation.” (See summary below or view the full text of the study at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7994299/ ) Jiang and colleagues recruited healthy adults without brain-computer interface experience and randomly assigned them to a wait-list control condition or to receive 8 weeks of Mindfulness-Based Stress Reduction (MBSR) program. After training they all participated in 6 or 10 weekly, 1-hour, brain-computer interface training sessions. Their brain electrical activity was recorded with an electroencephalogram (EEG). The electrical activity occurring in the motor cortex was connected to a computer which moved a cursor over the screen. The participants were asked to try to move the cursor left or right by imagining opening and closing the left or right hand, to move the cursor up by imagining opening and closing both hands and down by resting.

 

They found that compared to the wait-list control group the Mindfulness-Based Stress Reduction (MBSR) group had significantly greater improvement in the brain-computer interface task over sessions. They also found that the MBSR group had significantly greater alpha rhythm (8-12 hz. in the EEG) power in the frontal and limbic regions of the brain. They also found that over training there was decreased frontolimbic connectivity in the MBSR group while the wait-list control group had greater connectivity. Finally, the greater the increase in alpha rhythm power in the MBSR group, the greater the increase in the brain-computer interface task performance over sessions.

 

These results suggest that mindfulness training improves the individual’s ability to learn to control a computer with brain activity. Underlying this improved performance appears to be changes in the electrical activity of the brain at rest and during task performance. Mindfulness training is known to improve attention and reduce mind wandering. This may be how mindfulness training improves the individual’s ability to learn to control the computer with brain activity. It remains for future research to investigate this possibility.

 

So, improve the brains ability to directly control computers with mindfulness.

 

the emphasis on present-moment experience may allow expert meditators to self-regulate brain activity which could translate into enhanced [Brain-Computer Interface] control. Self-regulation supported by attentional control, emotional control, and self-awareness may additionally help users aim at a state of effortless relaxation, which has been hypothesized to improve [Brain-Computer Interface] control.” – James R. Stieger

 

CMCS – Center for Mindfulness and Contemplative Studies

 

This and other Contemplative Studies posts are also available on Google+ https://plus.google.com/106784388191201299496/posts and on Twitter @MindfulResearch

 

Study Summary

 

Jiang, H., Stieger, J., Kreitzer, M. J., Engel, S., & He, B. (2021). Frontolimbic alpha activity tracks intentional rest BCI control improvement through mindfulness meditation. Scientific reports, 11(1), 6818. https://doi.org/10.1038/s41598-021-86215-0

 

Abstract

Brain–computer interfaces (BCIs) are capable of translating human intentions into signals controlling an external device to assist patients with severe neuromuscular disorders. Prior work has demonstrated that participants with mindfulness meditation experience evince improved BCI performance, but the underlying neural mechanisms remain unclear. Here, we conducted a large-scale longitudinal intervention study by training participants in mindfulness-based stress reduction (MBSR; a standardized mind–body awareness training intervention), and investigated whether and how short-term MBSR affected sensorimotor rhythm (SMR)-based BCI performance. We hypothesize that MBSR training improves BCI performance by reducing mind wandering and enhancing self-awareness during the intentional rest BCI control, which would mainly be reflected by modulations of default-mode network and limbic network activity. We found that MBSR training significantly improved BCI performance compared to controls and these behavioral enhancements were accompanied by increased frontolimbic alpha activity (9–15 Hz) and decreased alpha connectivity among limbic network, frontoparietal network, and default-mode network. Furthermore, the modulations of frontolimbic alpha activity were positively correlated with the duration of meditation experience and the extent of BCI performance improvement. Overall, these data suggest that mindfulness allows participant to reach a state where they can modulate frontolimbic alpha power and improve BCI performance for SMR-based BCI control.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7994299/

 

Meditation Improves the Ability to Interface the Brain to Computers

Meditation Improves the Ability to Interface the Brain to Computers

 

By John M. de Castro, Ph.D.

 

Brain–computer interfaces (BCIs) are promising tools for assisting patients with paralysis, but suffer from long training times and variable user proficiency. Mind–body awareness training (MBAT) can improve BCI learning.” – ScienceDaily

 

It has long been a dream to develop methods to allow the brain to directly alter external devices. The efforts have been focused on developing a brain-computer interface such that recorded electrical activity of the brain is interfaced with a computer allowing control of the computer by the activity. It is hypothesized that a brain computer interface might be able to provide an alternative method to control muscles in patients with severe neuromuscular diseases.

 

Brain-computer interface methods have been developed but suffer from long training times before the participant is capable of affecting the computer activity. Meditation has been shown to alter the activity of the brain. Meditation training may make the individual better at controlling their brain activity. Hence, an interesting research question is to investigate whether meditation practitioners are better able to learn to control a computer with the brain’s electrical activity.

 

In today’s Research News article “Effects of Long-Term Meditation Practices on Sensorimotor Rhythm-Based Brain-Computer Interface Learning.” (See summary below or view the full text of the study at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7858648/ ) Jiang and colleagues recruited 2 groups of healthy adults without brain-computer interface experience; one with at least two years of meditation practice and one without meditation experience. They were all measured for mindfulness. They all participated in 6 weekly, 1-hour, brain-computer interface training. Their brain electrical activity was recorded with an electroencephalogram (EEG) the electrical activity in the motor cortex was connected to a computer which moved a cursor over the screen. The participants were asked to try to move the cursor left or right by imagining opening and closing the left or right hand, to move the cursor up by imagining opening and closing both hands and down by resting.

 

They found that the meditators had significantly better performance throughout training. Improvement occurred at approximately the same rate but the meditators started off at a higher baseline. The recording of alpha rhythm power over the motor cortex increased in both groups over training. In addition, they found that the higher the level of mindfulness before training, the better the performance with the meditators having significantly higher levels of mindfulness.

 

This is an interesting study but it should be kept in mind that the meditators may be different from non-meditators in ways other than the meditation practice. Being better able to control their brain activity may be characteristic of people who choose to meditate, Nevertheless, the results demonstrate that adults can alter the electrical activity in their motor cortex by imagining opening and closing their hands and that they can learn to increase this with feedback from a moving cursor. Meditators appear to have a leg up in learning this task with being better able to control their brain activity right from the beginning. This suggests that meditation practice improves the individual’s ability to alter their brain activity making them capable of learning a brain-computer interface task faster.

 

So, meditation improves the ability to interface the brain to computers.

 

Brain–computer interfaces (BCIs) are promising tools for assisting patients with paralysis, but suffer from long training times and variable user proficiency. Mind–body awareness training (MBAT) can improve BCI learning.” – James Stieger

 

CMCS – Center for Mindfulness and Contemplative Studies

 

This and other Contemplative Studies posts are also available on Google+ https://plus.google.com/106784388191201299496/posts and on Twitter @MindfulResearch

 

Study Summary

 

Jiang, X., Lopez, E., Stieger, J. R., Greco, C. M., & He, B. (2021). Effects of Long-Term Meditation Practices on Sensorimotor Rhythm-Based Brain-Computer Interface Learning. Frontiers in neuroscience, 14, 584971. https://doi.org/10.3389/fnins.2020.584971

 

Abstract

Sensorimotor rhythm (SMR)-based brain–computer interfaces (BCIs) provide an alternative pathway for users to perform motor control using motor imagery. Despite the non-invasiveness, ease of use, and low cost, this kind of BCI has limitations due to long training times and BCI inefficiency—that is, the SMR BCI control paradigm may not work well on a subpopulation of users. Meditation is a mental training method to improve mindfulness and awareness and is reported to have positive effects on one’s mental state. Here, we investigated the behavioral and electrophysiological differences between experienced meditators and meditation naïve subjects in one-dimensional (1D) and two-dimensional (2D) cursor control tasks. We found numerical evidence that meditators outperformed control subjects in both tasks (1D and 2D), and there were fewer BCI inefficient subjects in the meditator group. Finally, we also explored the neurophysiological difference between the two groups and showed that the meditators had a higher resting SMR predictor, more stable resting mu rhythm, and a larger control signal contrast than controls during the task.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7858648/