Quantitative change of EEG and respiration signals during mindfulness meditation

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. © 2014 ahani et al.; licensee biomed central ltd.