Support-vector-machine-based Meditation Experience Evaluation Using Electroencephalography Signals

Abstract

Meditation is used to improve psychological well-being, but there is no scientific quantitative evidence to prove the relation between them. Therefore, in this study, an effective classifier, namely a support vector machine (SVM), is applied to classify meditation experiences and help validate the interaction between emotional stability and a meditation experience. Three groups (10 subjects in each), created based on practice experience in meditation (S group with 10-30 years, J group with 1-7 years, and N group with 0 years of experience in Tibetan Nyingmapa meditation), were recruited to receive visual stimuli in the form of affective pictures. The images shown were selected from the International Affective Pictures System (IAPS), a confidential database. The response signals were acquired through physiological examination via electroencephalography (EEG). The subjects' data were entered into two classification systems, namely those based on the classification and regression tree (CART) method and the SVM method, respectively, and the outcomes were compared. From the classification results, SVM had a higher accuracy rate (98%) than that of CART (79%). The stability and robustness of SVM are higher than those of CART, as determined by performing over 100 repetitions and using various variable numbers. An evaluator based on SVM can thus assess a meditation experience through visual emotional stimulation. The results can help explain emotional stability during meditation.