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Quantitative change of EEG and respiration signals during mindfulness meditation.
- Source :
- Journal of NeuroEngineering & Rehabilitation (JNER); 2014, Vol. 11 Issue 1, p87-87, 1p
- Publication Year :
- 2014
-
Abstract
- <bold>Background: </bold>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.<bold>Methods: </bold>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.<bold>Results: </bold>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%).<bold>Conclusion: </bold>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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 17430003
- Volume :
- 11
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Journal of NeuroEngineering & Rehabilitation (JNER)
- Publication Type :
- Academic Journal
- Accession number :
- 103961197
- Full Text :
- https://doi.org/10.1186/1743-0003-11-87