A cross-sectional evaluation of meditation experience on electroencephalography data by artificial neural network and support vector machine classifiers.
Yu-Hao Lee, Ya-Ju Hsieh, Yung-Jong Shiah, Yu-Huei Lin, Chiao-Yun Chen, Yu-Chang Tyan, JiaCheng GengQiu, Chung-Yao Hsu, Sharon Chia-Ju Chen
Medicine April 1, 2017 DOI: 10.1097/md.0000000000006612 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Cross-sectional analysis Peer reviewed |
|---|---|
| Population | Three meditation groups categorized by meditation experience in 10-year intervals (novice, junior, senior) |
| Topics | Meditation |
| Keywords | Humans Image interpretation, computer-assisted Electroencephalography Cross-sectional studies Artificial intelligence Adult Middle aged Support vector machine |
| Key findings | The authors report that both artificial neural network and support vector machine classifiers achieved accuracy above 98% in distinguishing meditation experience from electroencephalography alpha-spectrum features, using a 0.5-minute sliding window and 2-second shifting interval. They propose this as a rapid evaluation system for meditation experience and a beneficial use of artificial techniques for big-data analysis. |
Abstract
To quantitate the meditation experience is a subjective and complex issue because it is confounded by many factors such as emotional state, method of meditation, and personal physical condition. In this study, we propose a strategy with a cross-sectional analysis to evaluate the meditation experience with 2 artificial intelligence techniques: artificial neural network and support vector machine. Within this analysis system, 3 features of the electroencephalography alpha spectrum and variant normalizing scaling are manipulated as the evaluating variables for the detection of accuracy. Thereafter, by modulating the sliding window (the period of the analyzed data) and shifting interval of the window (the time interval to shift the analyzed data), the effect of immediate analysis for the 2 methods is compared. This analysis system is performed on 3 meditation groups, categorizing their meditation experiences in 10-year intervals from novice to junior and to senior. After an exhausted calculation and cross-validation across all variables, the high accuracy rate >98% is achievable under the criterion of 0.5-minute sliding window and 2 seconds shifting interval for both methods. In a word, the minimum analyzable data length is 0.5 minute and the minimum recognizable temporal resolution is 2 seconds in the decision of meditative classification. Our proposed classifier of the meditation experience promotes a rapid evaluation system to distinguish meditation experience and a beneficial utilization of artificial techniques for the big-data analysis.
Comparable studies
Other cross-sectional and survey studies on meditation, most cited first.
| Study | Year | Design | Participants |
|---|---|---|---|
| Construct Validity of the Five Facet Mindfulness Questionnaire in Meditating and Nonmeditating Samples Experienced meditators and nonmeditating comparison groups | 2008 | Cross-sectional study | |
| Neural Correlates of Dispositional Mindfulness During Affect Labeling Adult participants | 2007 | Observational cross-sectional | n = 27 |
| Assessing mindfulness in children and adolescents: Development and validation of the Child and Adolescent Mindfulness Measure (CAMM). School-aged children and adolescents | 2011 | Cross-sectional validation study | n = 1,413 |
| Mindfulness and self-compassion as predictors of psychological wellbeing in long-term meditators and matched nonmeditators Experienced meditators and demographically matched nonmeditators | 2012 | Cross-sectional study | n = 152 |
| Mechanisms of Action in the Inverse Relationship Between Mindfulness and Psychological Distress Undergraduates | 2008 | Cross-sectional, correlational |
Citations in the library
Cited by 11
- EEG-based investigation of effects of mindfulness meditation training on state and trait by deep learning and traditional machine learning Frontiers in Human Neuroscience August 31, 2023
- Neurophysiological mechanisms of focused attention meditation: A scoping systematic review Imaging Neuroscience 2025
- Uncovering a stability signature of brain dynamics associated with meditation experience using massive time-series feature extraction. Neural Networks March 1, 2024
- Exploring neural markers of dereification in meditation based on EEG and personalized models of electrophysiological brain states. Scientific Reports October 16, 2024
- Using Electroencephalography to Advance Mindfulness Science: A Survey of Emerging Methods and Approaches. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging April 1, 2025
and 6 more in the library
Cites 16
- Alterations in Brain and Immune Function Produced by Mindfulness Meditation Psychosomatic Medicine July 1, 2003
- Long-term meditators self-induce high-amplitude gamma synchrony during mental practice Proceedings of the National Academy of Sciences November 8, 2004
- Regulation of the Neural Circuitry of Emotion by Compassion Meditation: Effects of Meditative Expertise PLoS One March 25, 2008
- EEG Derived Neuronal Dynamics during Meditation: Progress and Challenges Advances in Preventive Medicine 2015
- Focused attention, open monitoring and automatic self-transcending: Categories to organize meditations from Vedic, Buddhist and Chinese traditions. Consciousness and Cognition December 2010
and 11 more in the library