A recent article echoes many of the ideas that I have been discussion on this blog: Brandmeyer, T., & Delorme, A. (2013). Meditation and neurofeedback. Frontiers in psychology, 4. It can be downloaded from this site:
https://www.scienceopen.com/document_file/a9d8a886-e5b1-4a46-9914-6fa0ef9956eb/PubMedCentral/a9d8a886-e5b1-4a46-9914-6fa0ef9956eb.pdf.
Brandmeyer and Delorme focus on the possibility that "machine assisted programs such as neurofeedback may help individuals develop their meditation practice more rapidly." They point out that many of the disorders that neurofeedback has been used to treat also benefit from meditation training. Neurofeedback and meditation are both methods of training mental states. Both techniques facilitate and improve concentration and emotional regulation. The focus of training in most cognitive enhancement neurofeedback protocols share similarities with the EEG frequency bands that show the most significant change during the early stages of meditation practice.
They assume that reliable and reproducible EEG signatures associated with specific meditation practices can be identified and that it might be possible to train users to make their EEG brainwaves similar to the brainwaves of expert practitioners. While these assumption appear plausible, the diversity of meditation techniques and the complexity of brain activity during meditation may prove challenging. If these challenges could be overcome, the objective would not be to replace meditation practice but provide feedback to practitioners on how well they are doing.
Another type of neurofeedback program could help detect mind-wandering, setting off an alarm to users when their minds start to wander. The authors suggest that the feedback should be subtle so as not to disturb the subjects' meditation. Once again this would not replace meditation practice, but rather "facilitate and support it in its early to middle states of practice."
They conclude with suggestions that neurofeedback could be especially effective for beginners who are struggling with meditation practice. They point to smartphones and apps and social networking as opening up possibilities for the widespread adoption of neurofeedback as an aid to meditation.
Many of the points made by the authors have been made in previous posts on this blog. The biggest challenge, as they acknowledge, is identifying EEG signatures for meditation. In my own experience this is highly problematic. For example, slow alpha or high end theta is seen in experienced practitioners, but these slow waves are also associated with mind-wandering and drowsiness. There is some suggestion that combinations of slow waves and very fast waves in the gamma range may characterize some forms of meditation, but due to problems with artifact and the limited capabilities of low cost equipment, it may not be possible to train these combinations. There is a risk that, in the absence of very sophisticated and in-depth research on EEG signatures, training using this approach may do little good or even some harm.
The BrainBot app promises to provide warnings of mind-wandering. The developers seem to be on the right track, but how effective the app is remains to be seen. As I have described in previous posts, I have experimented with a similar approach. However, my experiences don't really count as research.
Brandmeyer and Delorme have identified the issues in their article and opened up to a wider audience what promises to be a fruitful discussion.
https://www.scienceopen.com/document_file/a9d8a886-e5b1-4a46-9914-6fa0ef9956eb/PubMedCentral/a9d8a886-e5b1-4a46-9914-6fa0ef9956eb.pdf.
Brandmeyer and Delorme focus on the possibility that "machine assisted programs such as neurofeedback may help individuals develop their meditation practice more rapidly." They point out that many of the disorders that neurofeedback has been used to treat also benefit from meditation training. Neurofeedback and meditation are both methods of training mental states. Both techniques facilitate and improve concentration and emotional regulation. The focus of training in most cognitive enhancement neurofeedback protocols share similarities with the EEG frequency bands that show the most significant change during the early stages of meditation practice.
They assume that reliable and reproducible EEG signatures associated with specific meditation practices can be identified and that it might be possible to train users to make their EEG brainwaves similar to the brainwaves of expert practitioners. While these assumption appear plausible, the diversity of meditation techniques and the complexity of brain activity during meditation may prove challenging. If these challenges could be overcome, the objective would not be to replace meditation practice but provide feedback to practitioners on how well they are doing.
Another type of neurofeedback program could help detect mind-wandering, setting off an alarm to users when their minds start to wander. The authors suggest that the feedback should be subtle so as not to disturb the subjects' meditation. Once again this would not replace meditation practice, but rather "facilitate and support it in its early to middle states of practice."
They conclude with suggestions that neurofeedback could be especially effective for beginners who are struggling with meditation practice. They point to smartphones and apps and social networking as opening up possibilities for the widespread adoption of neurofeedback as an aid to meditation.
Many of the points made by the authors have been made in previous posts on this blog. The biggest challenge, as they acknowledge, is identifying EEG signatures for meditation. In my own experience this is highly problematic. For example, slow alpha or high end theta is seen in experienced practitioners, but these slow waves are also associated with mind-wandering and drowsiness. There is some suggestion that combinations of slow waves and very fast waves in the gamma range may characterize some forms of meditation, but due to problems with artifact and the limited capabilities of low cost equipment, it may not be possible to train these combinations. There is a risk that, in the absence of very sophisticated and in-depth research on EEG signatures, training using this approach may do little good or even some harm.
The BrainBot app promises to provide warnings of mind-wandering. The developers seem to be on the right track, but how effective the app is remains to be seen. As I have described in previous posts, I have experimented with a similar approach. However, my experiences don't really count as research.
Brandmeyer and Delorme have identified the issues in their article and opened up to a wider audience what promises to be a fruitful discussion.
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