Meditation and Cognitive Enhancement: A Machine Learning Based Classification Using EEG
Swati Singh, Vinay Gupta, Tharun Kumar Reddy, B. Bhushan, L. Behera
IEEE International Conference on Systems, Man and Cybernetics October 9, 2022 DOI: 10.1109/smc53654.2022.9945131 (opens in new tab) via Semantic Scholar
Summary
AI-generated from the abstractAfter two weeks of regular mantra meditation practice, novice meditators' brain activity becomes more distinguishable from their baseline state, as shown by machine learning classification of EEG features. The study of 20 participants (10 experienced, 10 novice) found that classification accuracy between baseline and meditative EEG increased significantly for novices over the practice period, indicating enhanced meditation experience. Even this short practice improved cognitive abilities in novices, measured through the Brain-Based Intelligence Test (BBIT) and reflected in their EEG correlates. The analysis examined EEG band powers and connectivity features to evaluate meditation effects.
Study at a glance
| Characteristics | Observational cohort Peer reviewed |
|---|---|
| Sample size | 20 |
| Population | 10 experienced and 10 novice meditators |
| Intervention | Mantra meditation |
| Duration | Two-week long meditation practice |
| Keywords | Computer science Medicine |
| Key finding | Machine learning classification accuracy between baseline and meditative EEG increased significantly for novice meditators after two weeks of mantra meditation practice, indicating enhanced meditation experience and cognitive abilities. |
Abstract
Meditation methods, which have their origins in ancient traditions are gaining popularity as a result of their potential mental and physical health advantages. EEG neural correlates underlying enhanced cognitive abilities such as sustained attention and working memory need to be analyzed scrutinizingly to evaluate the effects of meditation practices. In this article, we thus provide an analysis of EEG features such as various band powers and connectivity based features to evaluate the meditation effects. Also, we provide a classification framework to classify the meditation states from the baseline EEG states. We report our results on an in house dataset of 20 participants(10 experienced and 10 novice) who underwent a two-week long mantra meditation practice. Strikingly we have found out that, as the novice participants practice meditation overtime, the accuracies of machine learning classification between the baseline EEG versus meditative EEG of the novice increase significantly, as an indication of their enhanced meditation experiences. Also, we found out that even such short and regular meditation practices, the cognitive abilities of novice meditators get enhanced which are evaluated through Brain-Based Intelligence Test (BBIT) psychometric tests and the results that got reflected in their EEG correlates.