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IEEE International Conference on Systems, Man and Cybernetics

4 papers in the library · publishing 2022-2024

Papers

VR-Based Mantra Meditation for Mental Wellness

IEEE International Conference on Systems, Man and Cybernetics October 6, 2024 Ankita Garg, Ajoy Kumar, Shubham Garg et al.

A ten-minute virtual reality session featuring audible mantra repetition reduced self-reported stress, anxiety, and depression and altered brainwave and heart rate variability measures compared to a control condition. The test group showed a significant increase in the frontal-alpha-to-temporal-theta ratio and a significant decrease in the alpha-to-beta ratio, alongside changes in heart rate variability. These results suggest that combining mantra repetition with virtual reality may support cognitive wellness.

VRZM: Exploring the Effect of Zen Meditation on EEG Patterns in Immersive Environments

IEEE International Conference on Systems, Man and Cybernetics October 6, 2024 Ajoy Kumar, Sahil Sankhyan, Kirti Tripathi et al.

A virtual reality guided Zen meditation (VRZM) session reduced depression, anxiety, and stress levels, and increased a frontal alpha-to-temporal theta brainwave ratio indicating relaxation, compared with a VR-only environment without meditation audio. Forty adults were randomly assigned to either VRZM or a VR-only condition. The VRZM group showed significant improvements on the Depression Anxiety Stress Scale and EEG measures, while the VR-only group showed no significant change. The results suggest VR-guided meditation can promote calmness and may be useful for mental health interventions.

An EEG Classifier to Discriminate Between Focused Attention Meditation and Problem-solving

IEEE International Conference on Systems, Man and Cybernetics October 9, 2022 Gansheng Tan, Shui-Bo Wang, Valentin Vierge et al.

A Random Forest classifier trained on two-second samples of EEG data can discriminate between Focused Attention Meditation (FAM) and a problem-solving task with high accuracy when personalized to each individual. Individual classifiers achieved an average accuracy of 93% across 14 subjects, whereas general classifiers trained on inter-individual data performed worse (74% and 54% depending on whether the tested subject's data was included in training). The most discriminating EEG features were Beta mean band amplitude and Theta-Gamma phase-amplitude coupling, especially in occipital and left centro-temporal brain regions. The findings favor personalized classifiers for real-time detection of meditative states.

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

IEEE International Conference on Systems, Man and Cybernetics October 9, 2022 Swati Singh, Vinay Gupta, Tharun Kumar Reddy et al.

After two weeks of regular mantra meditation practice, novice meditators' brain activity becomes more distinguishable from their baseline state, as shown by machine learning classification of EEG features. The study of 20 participants (10 experienced, 10 novice) found that classification accuracy between baseline and meditative EEG increased significantly for novices over the practice period, indicating enhanced meditation experience. Even this short practice improved cognitive abilities in novices, measured through the Brain-Based Intelligence Test (BBIT) and reflected in their EEG correlates. The analysis examined EEG band powers and connectivity features to evaluate meditation effects.