VITAL ASPECT OF RESPIRATION AND CHANGE IN BRAIN SIGNAL DURING MEDITATION USING EEG AND MACHINE LEARNING
S. Dinesh Nayak
International Journal of Apllied Mathematics October 22, 2025 DOI: 10.12732/ijam.v38i7s.517 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Observational study Qualitative Peer reviewed |
|---|---|
| Population | Inexperienced meditators, former yoga practitioners, and monks |
| Intervention | mindfulness meditation |
| Duration | Specific week of meditation |
| Topics | Meditation |
| Keywords | Breathing Electroencephalography Support vector machine Random forest Feature linguistics Vulnerability computing Artificial intelligence Audiology Signal programming language Machine learning Qualitative analysis Cognitive psychology Pattern recognition psychology |
| Key findings | Breathing feature vectors and EEG data may be reliable objective markers of meditation skill. |
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.
Comparable studies
Other observational and cohort studies on meditation, most cited first.
| Study | Year | Design | Participants |
|---|---|---|---|
| Using Self-Report Assessment Methods to Explore Facets of Mindfulness Undergraduate students | 2006 | Observational cohort | |
| Effectiveness of a meditation-based stress reduction program in the treatment of anxiety disorders Patients with generalized anxiety disorder or panic disorder with or without agoraphobia | 1992 | Observational cohort | n = 22 |
| Meditation experience is associated with increased cortical thickness Participants with extensive Insight meditation experience and matched controls | 2005 | Observational cohort | n = 20 |
| Meditation experience is associated with differences in default mode network activity and connectivity Experienced meditators and matched meditation-naive controls | 2011 | Observational cohort | |
| Long-term meditators self-induce high-amplitude gamma synchrony during mental practice Long-term Buddhist practitioners and controls | 2004 | Observational cohort |