Quantitative change of EEG and respiration signals during mindfulness meditation. Ahani, A., Wahbeh, H., Nezamfar, H., Miller, M., Erdogmus, D., & Oken, B. Journal of NeuroEngineering and Rehabilitation, 11(1):87, December, 2014. ![Quantitative change of EEG and respiration signals during mindfulness meditation [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@article{ahani_quantitative_2014,
title = {Quantitative change of {EEG} and respiration signals during mindfulness meditation},
volume = {11},
issn = {1743-0003},
url = {https://jneuroengrehab.biomedcentral.com/articles/10.1186/1743-0003-11-87},
doi = {10.1186/1743-0003-11-87},
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.},
language = {en},
number = {1},
urldate = {2022-03-22},
journal = {Journal of NeuroEngineering and Rehabilitation},
author = {Ahani, Asieh and Wahbeh, Helane and Nezamfar, Hooman and Miller, Meghan and Erdogmus, Deniz and Oken, Barry},
month = dec,
year = {2014},
pages = {87},
}
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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. 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