Temporal-domain Analysis of Meditation and Mind-wandering EEG Signals for Different Meditation Traditions
Katinder Kaur, P. Khandnor
2023 7th International Conference on Computer Applications in Electrical Engineering-Recent Advances October 27, 2023 DOI: 10.1109/cera59325.2023.10455199 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Observational study Peer reviewed |
|---|---|
| Intervention | Meditation |
| Topics | Meditation |
| Key findings | Temporal-domain EEG features, especially those related to signal complexity, can classify meditation versus mind-wandering states with 93% to 97% accuracy across different meditation traditions. |
Abstract
Meditation is an age-old practice that continues to attract significant interest in the field of neuroscience due to its stress-reducing and therapeutic benefits. In line with this growing area of research, our study aims to contribute by identifying potential neural markers associated with meditation. To achieve this goal, we investigate various features extracted from the time domain representation of EEG signals during meditation across different meditation traditions, including Himalayan Yoga meditation, Isha Shoonya meditation, and Vipassana meditation, and analyze them. Further, we use these features to discriminate EEG signals recorded during meditation from those during mind-wandering states. To facilitate our exploration, we use EEG signals from an online repository and preprocess them to eliminate noise and artifacts. Next, we extract an array of linear and non-linear features such as mean, standard deviation, RMS, kurtosis, skewness, zero-crossing rate, and sample entropy, which serve as input for two powerful binary classification algorithms: Support Vector Machine and Random Forest. We achieve classification accuracies ranging from 93% to 97% for different groups, indicating the efficacy of utilizing temporal- domain features of EEG signals for the successful differentiation of tasks. Furthermore, we conduct an analysis to identify the most crucial features, particularly their potential as neural markers. Our findings suggest that features associated with signal complexity demonstrate the highest efficacy in distinguishing between conditions and groups.
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 |
Citations in the library
Cites 10
- Meditation leads to reduced default mode network activity beyond an active task Cognitive, Affective, & Behavioral Neuroscience April 22, 2015
- Meditation Increases the Entropy of Brain Oscillatory Activity Neuroscience February 4, 2020
- EEG Derived Neuronal Dynamics during Meditation: Progress and Challenges Advances in Preventive Medicine 2015
- Large effects of brief meditation intervention on EEG spectra in meditation novices IBRO Reports October 27, 2020
- Exploration of Brain Network Measures Across Three Meditation Traditions NeuroRegulation September 29, 2022
and 5 more in the library