TY - JOUR
T1 - Quantitative change of EEG and respiration signals during mindfulness meditation
AU - Ahani, Asieh
AU - Wahbeh, Helane
AU - Nezamfar, Hooman
AU - Miller, Meghan
AU - Erdogmus, Deniz
AU - Oken, Barry
N1 - Funding Information:
This work was supported in part by National Institute of Health grants K01AT004951 and K24AT005121 and National Science Foundation grants IIS-0914808, CNS-1136027 and IIS-1149570. The authors acknowledge Elena Goodrich for teaching the mindfulness meditation sessions.
PY - 2014/5/14
Y1 - 2014/5/14
N2 - 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.
AB - 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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U2 - 10.1186/1743-0003-11-87
DO - 10.1186/1743-0003-11-87
M3 - Article
C2 - 24939519
AN - SCOPUS:84902301896
SN - 1743-0003
VL - 11
JO - Journal of NeuroEngineering and Rehabilitation
JF - Journal of NeuroEngineering and Rehabilitation
IS - 1
M1 - 87
ER -