VITAL ASPECT OF RESPIRATION AND CHANGE IN BRAIN SIGNAL DURING MEDITATION USING EEG AND MACHINE LEARNING

Abstract

Practicing breathing can enhance an individual's antioxidant status in addition to helping them cope with life's pressures. An increase in antioxidant status is useful in reducing many free radicals that are formed when the food is processed by our body or when we are constantly around things like pollution, smoke, etc. Due to the less oxidation states in the body, the practitioner gets a boost in the immune system with less vulnerability to disease causing microbes [2]. A change of EEG and respiration signals that are rewarded after breathing exercises (e.g. Kriya Yoga), show the qualitative change in human life. After a specific week of meditation, EEG and breathing data were gathered and evaluated on a number of inexperienced mediators, former yoga practitioners, and monks. Spectral analysis, phase analysis, and classification were used to assess the objective marker for meditation [6]. In order to determine if breathing feature vectors and EEG data are reliable objective markers of meditation skill, this research article will incorporate methods employed in machine learning, comprising Decision Trees, Support Vector Machines(SVM), and Random Forest classifiers. This study examines the mindfulness meditation measure (mm), a mental exercise that allows one to sustain a state of relaxation.