Ensemble Learning based EEG Classification – Investigating the Effects of Combined Yoga and Rajyog Meditation
Shobhika Madhu, Prashant Kumar, Sushil Chandra
Journal of Scientific & Industrial Research 2025 DOI: 10.56042/jsir.v84i1.10829 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Pre-post intervention study Peer reviewed |
|---|---|
| Sample size | 69 |
| Population | Students |
| Interventions | Yoga Rajyoga meditation |
| Topics | Meditation |
| Keywords | Electroencephalography Ensemble learning Artificial intelligence Pattern recognition psychology |
| Citations | 4 |
| Key points | A BiLSTM-DT ensemble model classified pre- and post-yoga/meditation EEG states with 82% accuracy, outperforming CNN-based models. |
Abstract
The ability to detect and prevent mental health deterioration has been one of the major achievements of digital psychiatry using artificial intelligence and machine learning. The aim of this paper is to address the issue of preventing the mental health disorders of young generation by developing a system to predict the changes in an individual's states of psychological health. Pre-and post-yoga and Rajyoga meditation states were analyzed for classification of data. Also, the paper investigates if bidirectional long-short-term memory BiLSTM-based ensemble models outperform the CNN-based models in prediction modeling. The EEG data was collected from 69 students for pre- and post-intervention. To determine an objective marker for yoga and meditation, collected data were analyzed using spectrum analysis, and classification. The post meditation group exhibited highest band powers and wavelet coefficients, indicating the differences in meditation and control conditions. Additionally, in this study, an ensemble model classifier has been developed utilizing EEG data that was more accurate (82%) than other models at differentiating between meditation and control situations. To the best of the knowledge of the authors, this is the first research to apply ensemble model-based classifiers to distinguish between states of meditation and non-meditation. The performance of BiLSTM-DT was the highest among all other models in terms of precision, recall, f-measure, and accuracy. Therefore, the BiLSTM-DT ensemble model is a viable objective marker for psychological health states.
Comparable studies
Other non-randomized and open-label trials on meditation, most cited first.
| Study | Year | Design | Participants |
|---|---|---|---|
| Mindfulness-Based Stress Reduction in Relation to Quality of Life, Mood, Symptoms of Stress, and Immune Parameters in Breast and Prostate Cancer Outpatients Early-stage breast and prostate cancer patients | 2003 | Pre-post intervention study | n = 59 |
| Exploring self‐compassion and empathy in the context of mindfulness‐based stress reduction (MBSR) Community sample | 2010 | Pre-post intervention study | |
| Mindfulness-Based Stress Reduction Lowers Psychological Distress In Medical Students Second-year medical students | 2003 | Prospective nonrandomized cohort-controlled study | n = 302 |
| Intensive meditation training, immune cell telomerase activity, and psychological mediators. Retreat participants and matched controls | 2011 | Controlled trial with wait-list control group | n = 60 |
| The Effectiveness of Mindfulness Training for Children with ADHD and Mindful Parenting for their Parents Children aged 8-12 with ADHD and their parents | 2011 | Within-group waitlist controlled trial | n = 22 |
Citations in the library
Cited by 2
- Exploring EEG biomarkers in meditation, yoga, and mantra practices: a review of neural correlates and methodologies Neural Computing and Applications March 5, 2026
- A deep learning approach for analyzing brainwave signals during audio meditation in expectant mothers: variational mode decomposition with a CNN-BiLSTM model. Biomedical physics & engineering express March 31, 2026