Impact Analysis of Meditation on Physiological Signals
JOIV: International Journal on Informatics Visualization June 28, 2018 Rahul Ingle, R N Awale
A classifier that combines respiration and EEG signals can distinguish Vipassana meditation from a control condition with 92% accuracy, compared to 85% accuracy using EEG signals alone. Forty novice meditators underwent a three-week meditation intervention, and their respiration and EEG data were analyzed using wavelet transforms and a support vector machine. Differences between meditation and control states appeared across alpha, beta, theta, delta, and gamma frequency bands. The combined respiration-EEG classifier offers a feasible objective marker for verifying meditation ability and may help advance mind-body medicine by providing a quantitative measure of meditative state.