Combining Neurofeedback and Meditation
How we can enhance the practice of meditation with real-time brain imaging
Posted Feb 23, 2020
Beginning meditators often complain that they do not know if they are “doing it right” or give up before realizing significant benefits. Advanced meditators often reach a plateau and struggle to reach “the next level” of their practice. Traditionally, meditation practitioners sought a guru or sangha for support and direction. Yet, many of us may not be interested in or have access to that kind of approach. Modern researchers and practitioners are finding a possible new solution to these challenges by using brain biofeedback to increase awareness of subtle states of consciousness and speed the learning process.
Simply put, biofeedback is any kind of mechanism that allows you to receive information (feedback) about your physiology (bio). This can include heart rate, skin temperature, or other physiological measures of nervous system arousal. With brainwave biofeedback, or EEG biofeedback, we are able to monitor brain activity and use this information to gently guide the user into specific states of meditation. This process is generally called neuromeditation.
By tracking brainwave activity in specific regions of the brain, we can tell if someone is focused or relaxed. We can tell if the mind is wandering, if they are engaged in body-based emotions, or if they have entered a space of internal quiet. By monitoring this activity and connecting it directly to the intent of the meditation, we can help meditators learn to quickly enter a desired state of consciousness and maintain this state for increasing periods of time.
Let me provide a simple example of how this works with a focus style of meditation. In this type of meditation, a person learns to focus their attention on a single target, such as the breath, a candle flame, or a mantra. When the mind inevitably wanders, they recognize that the mind wandered, and gently return their attention to the target. By monitoring brainwave activity in specific regions of the brain, we can get a pretty good idea if the person is actually engaged in the meditation or is instead, caught in mind wandering. When the attention is focused, the program plays an audio signal, such as a piece of ambient music, indicating to the person that they are “on track.” This provides direct and nearly immediate feedback to the meditator, allowing them to refine their internal awareness.
While most of the real-world applications of neuromodulation have been pioneered in the neurofeedback community and made popular through consumer wearables, there have been a few studies demonstrating the power of neuromeditation in the lab. The first study to examine the feasibility of neurofeedback for meditation used real-time fMRI data to examine the subjective experience of meditators when a specific brain region was active vs. quiet. This part of the brain (the Posterior Cingulate Cortex; PCC) is considered a key player in self-referential thought.
The meditators in this study reported experiences of being “distracted,” “interpreting,” “controlling," and “efforting,” when the PCC was active. In contrast, they reported experiences of “concentration,” “undistracted awareness,” “effortless doing,” and “observing sensory experience” when the PCC was deactivated (Garrison et al., 2013). A follow-up study provided information to meditators about the activity level of the (PCC) during a Focus style of meditation. Both meditators and non-meditators reported a significant relationship between activation of the PCC and mind wandering as well as deactivation of the PCC and focused attention.
The researchers also found that experienced meditators (but not novice meditators) were able to intentionally decrease activation of the PCC through the use of the feedback (Garrison, et al., 2014). In another study using EEG neurofeedback, Lutterveld, et al., found that both novice and experienced meditators were able to control the experience of effortless awareness in connection with a feedback signal indicating decreased PCC activation (2017).
While researchers have exclusively focused on activity in the PCC and its relationship to mind wandering, EEG Biofeedback clinicians have expanded upon this research to include a variety of brain regions and brain wave patterns. While the PCC is undoubtedly a critical region in many forms of meditation, there are other brain regions that are also important for specific states of meditation. In addition, from an EEG perspective, different brainwave patterns can be associated with different states of consciousness. In fact, the Tarrant has identified five different neurofeedback protocols that correspond to specific meditative states (2017a).
Let’s consider an Open-Heart practice, such as Lovingkindness-Compassion (LK-C). These practices have consistently been found to activate the Anterior Cingulate Cortex (ACC) and the right Insula (see Lutz, et al., 2008). When these areas are activated during a compassion meditation, the meditator frequently describes strong feelings of joy, love, gratitude, or empathy. The graph below shows the increase of Gamma brainwave activity in the ACC and right Insula during an 18-minute Open Heart neuromeditation session with an experienced meditator. You can also follow the link to see a recorded example of this type of neuromeditation session.
The combination of neurofeedback with meditation promises to help define and refine meditation for the 21st century. In fact, the Dalai Lama has publicly stated his support for this type of technology-assisted meditation (Mind and Life Institute, 2004). In short, the practice of neuromeditation may provide a solution to challenges faced by meditators by enhancing awareness of inner states. In addition, both meditation and neurofeedback have been found to be effective in the treatment of a variety of mental health concerns (Brandmeyer, & Delorme, 2013). Consequently, combining the two may provide a powerful treatment intervention for ADHD, anxiety, depression, and PTSD (Tarrant, 2017b).
With this understanding, the field of neuromeditation has the potential to significantly increase motivation, interest, and impact. It will be exciting to see how this practice develops in the upcoming years.
Brandmeyer, T., & Delorme, A. (2013). Meditation and neurofeedback. Frontiers in psychology, 4, 688. https://doi.org/10.3389/fpsyg.2013.00688
Garrison, K. A., Scheinost, D., Worhunsky, P. D., Elwafi, H. M., Thornhill, T. A., 4th, Thompson, E., Saron, C., Desbordes, G., Kober, H., Hampson, M., Gray, J. R., Constable, R. T., Papademetris, X., & Brewer, J. A. (2013). Real-time fMRI links subjective experience with brain activity during focused attention. NeuroImage, 81, 110–118. https://doi.org/10.1016/j.neuroimage.2013.05.030
Garrison KA, Santoyo JF, Davis JH, Thornhill TA IV, Kerr CE and Brewer JA (2013) Effortless awareness: using real time neurofeedback to investigate correlates of posterior cingulate cortex activity in meditators' self-report. Front. Hum. Neurosci. 7:440. doi: 10.3389/fnhum.2013.00440
Lutz, A., Brefczynski-Lewis, J., Johnstone, T., & Davidson,, R.J. (2008). Regulation of the neural circuitry of emotion by compassion meditation: Effects of meditative expertise. PLoS ONE, 3(3), e1897. Doi:10.1371/journal.pone.0001897.
Mind and Life Institute. (2004). Neuroplasticity: the neuronal substrates of learning and transformation, in Mind and Life Dialogues. Available online at http://www.mindandlife.org/dialogues/past-conferences/ml12/
Tarrant, J. (2017a). Meditation interventions to rewire the brain: Integrating Neuroscience Strategies for ADHD, anxiety, depression and PTSD. Eau Claire, WI: PESI Publishing and Media.
Tarrant, J. (2017b). NeuroMeditation: An Introduction and Overview. In Collura, T. F. & Frederick, J. A., Ed., Clinician’s Companion to QEEG and Neurofeedback (annotated and with an introduction by J. Kiffer). New York: Taylor & Francis.
van Lutterveld, R., et al., (2016). Source-space EEG neurofeedback links subjective experience with brain activity duringeffortless awareness meditation, NeuroImage. http://dx.doi.org/10.1016/j.neuroimage.2016.02.047