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.