Towards the development of personalized and generalized interfaces for brain signals across different styles of meditation
Shruti Singh, Pankaja Pandey, Shivam Chaudhary, K. Miyapuram, J. Lomas
Indian Conference on Computer Vision, Graphics & Image Processing December 8, 2022 DOI: 10.1145/3571600.3571656 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Observational study Peer reviewed |
|---|---|
| Population | Expert meditators of Himalayan Yoga, Vipassana, Isha Shoonya, and non-expert control subjects |
| Topics | Meditation |
| Key findings | Machine learning models can distinguish neural oscillations of meditators from non-meditators, with within-subject classification achieving maximum accuracy and cross-subject accuracy reaching 18.3% above chance in some conditions. |
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.
Comparable studies
Other observational and cohort studies on meditation, most cited first.
| Study | Year | Design | Participants |
|---|---|---|---|
| Using Self-Report Assessment Methods to Explore Facets of Mindfulness Undergraduate students | 2006 | Observational cohort | |
| Effectiveness of a meditation-based stress reduction program in the treatment of anxiety disorders Patients with generalized anxiety disorder or panic disorder with or without agoraphobia | 1992 | Observational cohort | n = 22 |
| Meditation experience is associated with increased cortical thickness Participants with extensive Insight meditation experience and matched controls | 2005 | Observational cohort | n = 20 |
| Meditation experience is associated with differences in default mode network activity and connectivity Experienced meditators and matched meditation-naive controls | 2011 | Observational cohort | |
| Long-term meditators self-induce high-amplitude gamma synchrony during mental practice Long-term Buddhist practitioners and controls | 2004 | Observational cohort |
Citations in the library
Cited by 2
- Meditation-induced cognitive state classification using sparse brain connectivity features and electroencephalography Intelligent Data Analysis September 17, 2025
- Optimizing deep CNN architecture via hybrid Harris Hawks arithmetic algorithm for EEG meditation classification. Neuroscience March 27, 2026
Cites 9
- Effects of the Transcendental Meditation Technique on Trait Anxiety: A Meta-Analysis of Randomized Controlled Trials The Journal of Alternative and Complementary Medicine October 9, 2013
- Meditation Increases the Entropy of Brain Oscillatory Activity Neuroscience February 4, 2020
- Exploration of Brain Network Measures Across Three Meditation Traditions NeuroRegulation September 29, 2022
- Real-time Sensing and NeuroFeedback for Practicing Meditation Using simultaneous EEG and Eye Tracking 2022 IEEE Region 10 Symposium July 1, 2022
- EEG based interpretation of human brain activity during yoga and meditation using machine learning: A systematic review. Complementary Therapies in Clinical Practice February 14, 2021
and 4 more in the library