Meditation and Cognitive Enhancement: A Machine Learning Based Classification Using EEG

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.