Uncovering a stability signature of brain dynamics associated with meditation experience using massive time-series feature extraction.
Neil W. Bailey, Ben D Fulcher, Bridget Caldwell, Aron T. Hill, Bernadette M. Fitzgibbon, Hanneke Van Dijk, Paul B Fitzgerald
Neural Networks March 1, 2024 DOI: 10.1016/j.neunet.2023.12.007 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Observational cohort Peer reviewed |
|---|---|
| Sample size | 95 |
| Population | Meditators and non-meditators |
| Topics | Meditation |
| Keywords | Electroencephalography Hctsa Machine learning Massive feature extraction Time-series analysis |
| Citations | 7 |
| Key findings | Meditators exhibited higher temporal stability and altered distributional shape of EEG time-series values in a central-parietal component, while traditional band-power measures did not distinguish groups. |
Abstract
Previous research has examined resting electroencephalographic (EEG) data to explore brain activity related to meditation. However, previous research has mostly examined power in different frequency bands. The practical objective of this study was to comprehensively test whether other types of time-series analysis methods are better suited to characterize brain activity related to meditation. To achieve this, we compared >7000 time-series features of the EEG signal to comprehensively characterize brain activity differences in meditators, using many measures that are novel in meditation research. Eyes-closed resting-state EEG data from 49 meditators and 46 non-meditators was decomposed into the top eight principal components (PCs). We extracted 7381 time-series features from each PC and each participant and used them to train classification algorithms to identify meditators. Highly differentiating individual features from successful classifiers were analysed in detail. Only the third PC (which had a central-parietal maximum) showed above-chance classification accuracy (67 %, pFDR = 0.007), for which 405 features significantly distinguished meditators (all pFDR < 0.05). Top-performing features indicated that meditators exhibited more consistent statistical properties across shorter subsegments of their EEG time-series (higher stationarity) and displayed an altered distributional shape of values about the mean. By contrast, classifiers trained with traditional band-power measures did not distinguish the groups (pFDR > 0.05). Our novel analysis approach suggests the key signatures of meditators' brain activity are higher temporal stability and a distribution of time-series values suggestive of longer, larger, or more frequent non-outlying voltage deviations from the mean within the third PC of their EEG data. The higher temporal stability observed in this EEG component might underpin the higher attentional stability associated with meditation. The novel time-series properties identified here have considerable potential for future exploration in meditation research and the analysis of neural dynamics more broadly.
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 6
- The Mindful Brain at Rest: Neural Oscillations and Aperiodic Activity in Experienced Meditators Mindfulness October 1, 2024
- Experienced meditators show greater forward traveling cortical alpha wave strengths. Annals of the New York Academy of Sciences July 2, 2025
- Using Electroencephalography to Advance Mindfulness Science: A Survey of Emerging Methods and Approaches. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging April 1, 2025
- TMS-EEG Shows Mindfulness Meditation Is Associated With a Different Excitation/Inhibition Balance in the Dorsolateral Prefrontal Cortex Mindfulness February 1, 2025
- Neural Differences in Conflict Monitoring, Stimulus Expectancy, and Attention-Related Processes in Experienced Meditators Mindfulness July 3, 2026
and 1 more in the library
Cites 38
- Using Self-Report Assessment Methods to Explore Facets of Mindfulness Assessment January 27, 2006
- How Does Mindfulness Meditation Work? Proposing Mechanisms of Action From a Conceptual and Neural Perspective Perspectives on Psychological Science October 14, 2011
- Long-term meditators self-induce high-amplitude gamma synchrony during mental practice Proceedings of the National Academy of Sciences November 8, 2004
- A systematic review of neurobiological and clinical features of mindfulness meditations Psychological Medicine November 27, 2009
- A systematic review of the neurophysiology of mindfulness on EEG oscillations Neuroscience & Biobehavioral Reviews 2015
and 33 more in the library