Quantitative change of EEG and respiration signals during mindfulness meditation

A classifier combining EEG and respiration signals can distinguish between meditation and control conditions with 85% accuracy in older novice meditators after a 6-week mindfulness meditation intervention. Using only EEG signals achieved 78% accuracy. The classifier, based on spectral and phase analysis, may serve as an objective marker for meditation ability and could quantify meditation depth and experience in future research.

Study at a glance

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.