Effect of Heartfulness Meditation Among Long-Term, Short-Term and Non-meditators on Prefrontal Cortex Activity of Brain Using Machine Learning Classification: A Cross-Sectional Study
Cureus February 14, 2023 Anurag Shrivastava, Bikesh Kumar Singh, Dwivedi Krishna et al. 7 citations
A feasibility study examined whether electroencephalogram (EEG) connectivity patterns can distinguish long-term Heartfulness meditation practitioners, short-term practitioners, and non-meditators. EEG data from 34 participants were analyzed using functional connectivity parameters as features for machine learning classifiers. When classifying long-term meditators versus non-meditators, model accuracy ranged from 84% to 100%; for short-term meditators versus non-meditators, accuracy ranged from 80% to 93%. Decision trees, support vector machines, k-nearest neighbors, and ensemble classifiers outperformed linear discriminant analysis and logistic regression. The results suggest that machine learning applied to EEG functional connectivity may serve as a marker for meditation proficiency.