Common spatial pattern for classification of loving kindness meditation EEG for single and multiple sessions
Nalinda D. Liyanagedera, Ali Abdul Hussain, Amardeep Singh, Sunil Pranit Lal, Heather Kempton, Hans W. Guesgen
Brain Informatics September 9, 2023 DOI: 10.1186/s40708-023-00204-9 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Observational study Peer reviewed |
|---|---|
| Sample size | 32 |
| Population | Participants providing EEG data during meditation and non-meditation tasks |
| Intervention | LKM-Others) |
| Topics | Meditation |
| Keywords | Electroencephalography Pattern recognition psychology Linear discriminant analysis Artificial intelligence Cognitive psychology |
| Citations | 4 |
| Key findings | Meditation and pre-resting EEG data can be classified with high accuracy for both single and multiple sessions, with pre-resting states being more distinct from other mind tasks. |
Abstract
While a very few studies have been conducted on classifying loving kindness meditation (LKM) and non-meditation electroencephalography (EEG) data for a single session, there are no such studies conducted for multiple session EEG data. Thus, this study aims at classifying existing raw EEG meditation data on single and multiple sessions to come up with meaningful inferences which will be highly beneficial when developing algorithms that can support meditation practices. In this analysis, data have been collected on Pre-Resting (before-meditation), Post-Resting (after-meditation), LKM-Self and LKM-Others for 32 participants and hence allowing us to conduct six pairwise comparisons for the four mind tasks. Common Spatial Patterns (CSP) is a feature extraction method widely used in motor imaginary brain computer interface (BCI), but not in meditation EEG data. Therefore, using CSP in extracting features from meditation EEG data and classifying meditation/non-meditation instances, particularly for multiple sessions will create a new path in future meditation EEG research. The classification was done using Linear Discriminant Analysis (LDA) where both meditation techniques (LKM-Self and LKM-Others) were compared with Pre-Resting and Post-Resting instances. The results show that for a single session of 32 participants, around 99.5% accuracy was obtained for classifying meditation/Pre-Resting instances. For the 15 participants when using five sessions of EEG data, around 83.6% accuracy was obtained for classifying meditation/Pre-Resting instances. The results demonstrate the ability to classify meditation/Pre-Resting data. Most importantly, this classification is possible for multiple session data as well. In addition to this, when comparing the classification accuracies of the six mind task pairs; LKM-Self, LKM-Others and Post-Resting produced relatively lower accuracies among them than the accuracies obtained for classifying Pre-Resting with the other three. This indicates that Pre-Resting has some features giving a better classification indicating that it is different from the other three mind tasks.
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 35
- The toronto mindfulness scale: Development and validation Journal of Clinical Psychology 2006
- Mindfulness on-the-go: Effects of a mindfulness meditation app on work stress and well-being. Journal of Occupational Health Psychology May 3, 2018
- Frontal theta EEG activity correlates negatively with the default mode network in resting state International Journal of Psychophysiology July 13, 2007
- Quantitative change of EEG and respiration signals during mindfulness meditation Journal of NeuroEngineering and Rehabilitation May 14, 2014
- The neuroscience of meditation: classification, phenomenology, correlates, and mechanisms. Progress in Brain Research 2019
and 30 more in the library