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Quantitative change of EEG and respiration signals during mindfulness meditation
Open accessAbstract
BACKGROUND: This study investigates measures of mindfulness meditation (MM) as a mental practice, in which a resting but alert state of mind is maintained. A population of older people with high stress level participated in this study, while electroencephalographic (EEG) and respiration signals were recorded during a MM intervention. The physiological signals during meditation and control conditions were analyzed with signal processing. METHODS: EEG and respiration data were collected and analyzed on 34 novice meditators after a 6-week meditation intervention. Collected data were analyzed with spectral analysis, phase analysis and classification to evaluate an objective marker for meditation. RESULTS: Different frequency bands showed differences in meditation and control conditions. Furthermore, we established a classifier using EEG and respiration signals with a higher accuracy (85%) at discriminating between meditation and control conditions than a classifier using the EEG signal only (78%). CONCLUSION: Support vector machine (SVM) classifier with EEG and respiration feature vector is a viable objective marker for meditation ability. This classifier should be able to quantify different levels of meditation depth and meditation experience in future studies.
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“During these components, explicit instruction to slow ones breath is not given, however, it has been reported in multiple investigations that breath slows during the practice (Ditto et al., 2006; Ahani et al., 2014).”
From Maladaptive autonomic regulation in PTSD accelerates physiological aging (2015) · cites this paper as Ahani et al., 2014
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“Although mindfulness instructions do not involve purposefully slowing the breath, the rate of breathing commonly slows down automatically during mindfulness meditation (Ahani et al., 2014).”
From Being able to think when caught in the maelstrom - how adolescents used mindfulness when facing exams. (2024) · cites this paper as Ahani et al., 2014
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Metadata
- Authors
- Ahani, A., Wahbeh, H., Nezamfar, H., Miller, M., Erdogmus, D., & Oken, B.
- Affiliations
- Northeastern University (US)
- Oregon Health & Science University (US)
- Corresponding author
- Oken, Barry
- Venue
- Journal of NeuroEngineering and Rehabilitation
- Published
- 2014-05-14
- Volume
- 11
- Issue
- 1
- Pages
- 87-87
- ISSN
- 1743-0003 (ISSN-L)
- PMC
- PMC4060143
- OpenAlex
- W2153257584
- Cited by
- 159
- References
- 51
- Citations by year
- 2026: 16 · 2025: 19 · 2024: 21 · 2023: 19 · 2022: 16 · 2021: 16 · 2020: 13 · 2019: 7 · 2018: 7 · 2017: 10 · 2016: 10 · 2015: 4
- FWCI
- 9.47
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of NeuroEngineering and Rehabilitation (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Science Foundation (0914808, 1149570, 1136027)
- National Center for Complementary and Integrative Health (K24AT005121, K01AT004951)
- Topics
- Social Sciences › Psychology › Clinical Psychology › Mindfulness and Compassion Interventions
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Keywords
- mindfulness meditation, EEG, respiratory signals, support vector classifier, older adults, stress, frequency band analysis, phase analysis
- MeSH terms
- Aged, Brain, Electroencephalography, Female, Humans, Male, Middle Aged, Respiration, Signal Processing, Computer-Assisted, Meditation, Support Vector Machine, Mindfulness
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