Cited by 159Mindfulness and Compassion InterventionsEEG and Brain-Computer InterfacesEmotion and Mood Recognition

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Quantitative change of EEG and respiration signals during mindfulness meditation

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Ahani, Asieh, Wahbeh, Helane, Nezamfar, Hooman, Miller, Meghan, Erdogmus, Deniz, Oken, Barry·2014·Journal of NeuroEngineering and Rehabilitation

DOI 10.1186/1743-0003-11-87PMC PMC4060143

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Abstract

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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Figure 1 Six regions over scalp. Electrodes divided into six regions over scalp as frontal, central, parietal, occipital, right temporal and left temporal.CC BY · PubMed Central

Figure 2 Classification process flow chart.CC BY · PubMed Central

Figure 3 EEG spectral analysis. PSD estimation of EEG signal over alpha, beta and theta bands at different locations over scalp for meditation and control conditions averaged across subjects (N = 34). Star symbols show which comparisons are statistically significant.CC BY · PubMed Central

Figure 4 Respiration time frequency analysis. Time frequency Analysis of respiration signal shows more activity in lower frequencies during meditation. The figure shows the Stockwell coefficients amplitude averaged across subjects (N = 34) in meditation and control conditions.CC BY · PubMed Central

Figure 5 EEG phase analysis. Number of pairs of electrodes with higher PLV than the threshold averaged across subjects (N = 34). Star symbols show which comparisons are statistically significant.CC BY · PubMed Central

Figure 6 SVM classification results. SVM classification accuracy using EEG, Respiration and joint (N = 34). Besides the overall ANOVA being significant, all pairwise differences using paired t tests were significantly different. Star symbols show which comparisons are statistically significant.CC BY · PubMed Central

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Support-vector networks
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
A Global Measure of Perceived Stress
Cohen, S., Kamarck, T., & Mermelstein, R. (1983) · Journal of Health and Social Behavior · doi:10.2307/2136404 · Cited by 32,807
An Analysis of Transformations
Box, G. E. P., & Cox, D. R. (1964) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1964.tb00553.x · Cited by 15,287
Libsvm : A library for support vector machines
Ferrari, V. (2008) · Medical Entomology and Zoology · Cited by 10,123
An Information-Maximization Approach to Blind Separation and Blind Deconvolution
Bell, A. J., & Sejnowski, T. J. (1995) · Neural Computation · doi:10.1162/neco.1995.7.6.1129 · Cited by 8,986
Untitled work
(2008) · Medical Entomology and Zoology · Cited by 7,477
On the use of windows for harmonic analysis with the discrete Fourier transform
Harris, F. J. (1978) · Proceedings of the IEEE · doi:10.1109/proc.1978.10837 · Cited by 7,326
Digital Signal Processing: Principles, Algorithms, and Applications
Proakis, J. G., & Manolakis, D. G. (1992) · Cited by 4,601
An outpatient program in behavioral medicine for chronic pain patients based on the practice of mindfulness meditation: Theoretical considerations and preliminary results
Kabat-Zinn, J. (1982) · General Hospital Psychiatry · doi:10.1016/0163-8343(82)90026-3 · Cited by 4,255

Show all 51 references

Research works printed but not linked: 3

Welsh KA, Breitner JCS, Magruder-Habib KM: Detection of dementia in the elderly using telephone screning of cognitive status. Neuropsychiatry Neuropsychol Behav Neurol 1993, 6: 103-110.
Bach FR, Jordan MI: Kernel independent component analysis. J Mach Learn Res 2002, 3: 1-48.
Oken B, Zajdel D, Wild H: Induced and trait differences in EEG phase synchrony and power for experienced meditators versus non-meditators. 2007.

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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
ORCID: Ahani, Asieh
Nezamfar, Hooman
Oken, Barry
Venue: Journal of NeuroEngineering and Rehabilitation
Publisher: Springer Science and Business Media LLC
Published: 2014-05-14
Volume: 11
Issue: 1
Pages: 87-87
ISSN: 1743-0003 (ISSN-L)
DOI: 10.1186/1743-0003-11-87
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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Quantitative change of EEG and respiration signals during mindfulness meditation

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