Towards the development of personalized and generalized interfaces for brain signals across different styles of meditation
Authors: 
Shruti Singh, Pankaj Pandey, Shivam Chaudhary, Krishna P Miyapuram, James LomasAuthors Info & Claims
Article No.: 54, Pages 1 - 9
Abstract
Human-computer interaction investigates how people learn from technology, and how they use technology in everyday life. Researchers have used brain-computer interfaces to understand how technology can be designed to support human cognition and behavior. The most famous and consumer-friendly approach to measuring brain signals is electroencephalography (EEG) due to its non-invasive, portable, relatively inexpensive, and high temporal resolution. In this study, we develop machine learning models to distinguish between the neural oscillations of meditators and non-meditators. Previous studies have used power spectrum density, entropy, and functional connectivity to distinguish various meditation traditions. We use EEG data set comprising neural activity of expert meditators of Himalayan Yoga (HYT), Vipassana (VIP), Isha Shoonya (SYN), and non-expert control subjects (CTR). We analyze the data using 13 different machine learning models for within-subject and cross-subject. We present the results for six classification conditions for both meditation and mind-wandering. Features extracted from the mean of 64 EEG time series are fed into machine learning classifiers during training. We obtain maximum accuracy for within-subject classification in both meditation and mind-wandering. In cross-subject analysis, we obtained 18.3% above chance level in meditation between control and Isha Shoonya, and similarly above 18% chance level in mind-wandering between control and Vipassana. We discuss the implications of this result for the emerging consumer EEG headset facilitating meditation practice. Our results indicate that personalized models (within-subject) and generalized models (cross-subject) could guide naive (beginner) practitioners to meditate and aim to modulate brain signals by practicing to reach the expert level.
References
[1]
[n.d.]. EEG wearable headset. https://neuphony.com/
[2]
[n.d.]. Meditation made easy. https://choosemuse.com/
[3]
Susan Ajith, Pankaj Pandey, and Krishna Prasad Miyapuram. [n.d.]. Attention related experimental paradigms in meditation research. ([n. d.]).
[4]
Mansoor M Aman, R Jason Yong, Alan David Kaye, and Richard D Urman. 2018. Evidence-based non-pharmacological therapies for fibromyalgia. Current pain and headache reports 22, 5 (2018), 1–5.
[5]
Christian Anasi, David Zarka, Robin Álvarez, Carlos Cevallos, Guy Cheron, and Fernando Vásquez. 2018. Individual analysis of EEG brain dynamics produced by mindfulness-based stress reduction training program. In 2018 IEEE Third Ecuador Technical Chapters Meeting (ETCM). IEEE, 1–6.
[6]
Claire Braboszcz, B Rael Cahn, Jonathan Levy, Manuel Fernandez, and Arnaud Delorme. 2017. Increased gamma brainwave amplitude compared to control in three different meditation traditions. PloS one 12, 1 (2017), e0170647.
[7]
Tracy Brandmeyer, Arnaud Delorme, and Helané Wahbeh. 2019. The neuroscience of meditation: classification, phenomenology, correlates, and mechanisms. Progress in brain research 244 (2019), 1–29.
[8]
Shivam Chaudhary, Pankaj Pandey, Krishna Prasad Miyapuram, and Derek Lomas. 2022. Classifying EEG signals of mind-wandering across different styles of meditation. Brain Informatics (2022), 152–163. https://doi.org/10.1007/978-3-031-15037-1_13
[9]
Yunhui Chen, Jiayuan Zhang, Tiane Zhang, Liu Cao, Yanyan You, Chunjiang Zhang, Xinglong Liu, and Qi Zhang. 2020. Meditation treatment of Alzheimer disease and mild cognitive impairment: A protocol for systematic review. Medicine 99, 10 (2020).
[10]
Pragati Gupta, Pankaj Pandey, and Krishna Prasad Miyapuram. 2022. Reliable EEG neuromarker to discriminate meditative states across practitioners. (2022).
[11]
Lara Hilton, Susanne Hempel, Brett A Ewing, Eric Apaydin, Lea Xenakis, Sydne Newberry, Ben Colaiaco, Alicia Ruelaz Maher, Roberta M Shanman, Melony E Sorbero, 2017. Mindfulness meditation for chronic pain: systematic review and meta-analysis. Annals of Behavioral Medicine 51, 2 (2017), 199–213.
[12]
Lara Hilton, Alicia Ruelaz Maher, Benjamin Colaiaco, Eric Apaydin, Melony E Sorbero, Marika Booth, Roberta M Shanman, and Susanne Hempel. 2017. Meditation for posttraumatic stress: Systematic review and meta-analysis.Psychological Trauma: Theory, Research, Practice, and Policy 9, 4(2017), 453.
[13]
Eshvendar Reddy Kasala, Lakshmi Narendra Bodduluru, Yogeshwar Maneti, and Rajesh Thipparaboina. 2014. Effect of meditation on neurophysiological changes in stress mediated depression. Complementary therapies in clinical practice 20, 1 (2014), 74–80.
[14]
Dharma Singh Khalsa. 2015. Stress, meditation, and Alzheimer’s disease prevention: where the evidence stands. Journal of Alzheimer’s Disease 48, 1 (2015), 1–12.
[15]
Laura G Kiken and Natalie J Shook. 2014. Does mindfulness attenuate thoughts emphasizing negativity, but not positivity?Journal of research in personality 53 (2014), 22–30.
[16]
Padmavathi Kora, K Meenakshi, K Swaraja, A Rajani, and Mantena Satyanarayana Raju. 2021. EEG based interpretation of human brain activity during yoga and meditation using machine learning: A systematic review. Complementary therapies in clinical practice 43 (2021), 101329.
[17]
Yuanqing Li, Jiahui Pan, Fei Wang, and Zhuliang Yu. 2013. A hybrid BCI system combining P300 and SSVEP and its application to wheelchair control. IEEE Transactions on Biomedical Engineering 60, 11 (2013), 3156–3166.
[18]
Antoine Lutz, Heleen A Slagter, John D Dunne, and Richard J Davidson. 2008. Attention regulation and monitoring in meditation. Trends in cognitive sciences 12, 4 (2008), 163–169.
[19]
Jessica Migala. 2021. These 7 apps will deepen your meditation practice. https://www.verywellmind.com/best-meditation-apps-4767322
[20]
David W Orme-Johnson and Vernon A Barnes. 2014. Effects of the transcendental meditation technique on trait anxiety: a meta-analysis of randomized controlled trials. The Journal of Alternative and Complementary Medicine 20, 5 (2014), 330–341.
[21]
Ozan Özdenizci, Ye Wang, Toshiaki Koike-Akino, and Deniz Erdoğmuş. 2020. Learning invariant representations from EEG via adversarial inference. IEEE access 8(2020), 27074–27085.
[22]
Pankaj Pandey, Pragati Gupta, Shivam Chaudhary, Krishna Prasad Miyapuram, and Derek Lomas. 2022. Real-time sensing and neurofeedback for practicing meditation using simultaneous EEG and eye tracking. In 2022 IEEE Region 10 Symposium (TENSYMP). IEEE, 1–6.
[23]
Pankaj Pandey, Pragati Gupta, and Krishna Prasad Miyapuram. 2021. Brain connectivity based classification of meditation expertise. In International Conference on Brain Informatics. Springer, 89–98.
[24]
Pankaj Pandey, Pragati Gupta, and Krishna Prasad Miyapuram. 2022. Exploration of brain network measures across three meditation traditions. NeuroRegulation 9, 3 (2022), 113–113.
[25]
Pankaj Pandey and Krishna Prasad Miyapuram. 2020. Classifying oscillatory signatures of expert vs nonexpert meditators. In 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 1–7.
[26]
Pankaj Pandey and Krishna Prasad Miyapuram. 2021. Brain2depth: Lightweight cnn model for classification of cognitive states from eeg recordings. In Annual Conference on Medical Image Understanding and Analysis. Springer, 394–407.
[27]
Pankaj Pandey and Krishna Prasad Miyapuram. 2021. Nonlinear EEG analysis of mindfulness training using interpretable machine learning. In 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 3051–3057.
[28]
Pankaj Pandey, Julio Rodriguez-Larios, Krishna Prasad Miyapuram, and Derek Lomas. 2022. Detecting moments of distraction during meditation practice based on changes in the EEG signal. (11 2022). https://doi.org/10.36227/techrxiv.21572586.v1
[29]
Pankaj Pandey, Gulshan Sharma, Krishna P Miyapuram, Ramanathan Subramanian, and Derek Lomas. 2022. Music identification using brain responses to initial snippets. In ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 1246–1250.
[30]
Pankaj Pandey, Richa Tripathi, and Krishna Prasad Miyapuram. 2022. Classifying oscillatory brain activity associated with Indian Rasas using network metrics. Brain Informatics 9, 1 (2022), 1–20.
[31]
Gulshan Sharma, Pankaj Pandey, Ramanathan Subramanian, Krishna Prasad Miyapuram, and Abhinav Dhall. 2022. Neural encoding of songs is modulated by their enjoyment. In INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION. 414–419.
[32]
Jacob Van Doorn, Mengqi Xing, B Rael Cahn, Arnaud Delorme, Olusola Ajilore, and Alex D Leow. 2020. Everyone Can Meditate: Characterizing a Personalized Connectomic State Space among Meditation Groups and Non-meditators. bioRxiv (2020).
[33]
Peter Vestergaard-Poulsen, Martijn van Beek, Joshua Skewes, Carsten R Bjarkam, Michael Stubberup, Jes Bertelsen, and Andreas Roepstorff. 2009. Long-term meditation is associated with increased gray matter density in the brain stem. Neuroreport 20, 2 (2009), 170–174.
[34]
Rocío Martínez Vivot, Carla Pallavicini, Federico Zamberlan, Daniel Vigo, and Enzo Tagliazucchi. 2020. Meditation increases the entropy of brain oscillatory activity. Neuroscience 431(2020), 40–51.
Index Terms
- Towards the development of personalized and generalized interfaces for brain signals across different styles of meditation
Recommendations
Towards the detection of error-related potentials and its integration in the context of a P300 speller brain-computer interface
A P300 Speller is a brain-computer interface (BCI) that enables subjects to spell text on a computer screen by detecting P300 Event-Related Potentials in their electroencephalograms (EEG). This BCI application is of particular interest to disabled ...
Meditation: A Performance Booster for BCI Applications
CHI EA '18: Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing SystemsA key factor that a typical Brain-Computer Interface (BCI) demands of operators is concentration of mind. A focused mind is more effective in controlling a BCI. One way to improve mental concentration is meditation. Past studies have demonstrated the ...
Brain-Hack: Remotely Injecting False Brain-Waves with RF to Take Control of a Brain-Computer Interface
CPSIoTSec '23: Proceedings of the 5th Workshop on CPS&IoT Security and PrivacyThe promise of Brain-Computer Interfaces (BCIs) is counterbalanced by concerns about vulnerabilities. Recent studies have revealed that EEG-based BCIs are susceptible to security breaches. However, current attack approaches are challenging to execute in ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
December 2022
506 pages
Copyright © 2022 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 12 May 2023
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ICVGIP'22
ICVGIP'22: Thirteenth Indian Conference on Computer Vision, Graphics and Image Processing
December 8 - 10, 2022
Gandhinagar, India
Acceptance Rates
Overall Acceptance Rate 95 of 286 submissions, 33%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 138Total Downloads
- Downloads (Last 12 months)47
- Downloads (Last 6 weeks)5
Reflects downloads up to 31 Dec 2024
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format