Network analysis of meditative states in highly skilled meditators using EEG and horizontal visibility graphs.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference July 1, 2024 DOI: 10.1109/EMBC53108.2024.10782024 via PubMed
Summary
AI-generated from the abstractMeditation's benefits are increasingly recognized, but the brain's electrical activity during meditative states is not fully understood. Existing markers have limited predictive accuracy, suggesting important information is missing. This work converts EEG time series into scale-free networks using horizontal visibility graphs, which distinguish deterministic from random systems and model new aspects of brain oscillations. The authors introduce a class of network-based predictors that outperform popular spectral and nonlinear features like complexity or entropy. These predictors show statistical significance for several meditation types, using data from highly skilled meditators, and are suitable for real-time analysis and applications such as neurofeedback.
Study at a glance
| Characteristics | Observational cohort Peer reviewed |
|---|---|
| Population | Highly skilled meditators |
| Citations | 2 |
| Key finding | Network-based predictors derived from EEG horizontal visibility graphs outperform spectral and nonlinear features in predicting meditative states. |
Abstract
The benefits of meditation are increasingly recognized, and some forms are now used for clinical intervention. However, the electrophysiological correlates of meditative states are not yet well understood, and the limited predictive accuracy of known markers of meditation suggest that not all information relevant to meditation has been captured by previous work.Here, we convert electroencephalography (EEG) time series into scale-free networks using horizontal visibility graphs (HVGs), which are well-suited to distinguishing deterministic dynamical systems from stochastic systems, allowing them to model novel aspects of cortical oscillatory activity. Based on HVGs, we introduce and evaluate a general class of predictors, which can be used to augment existing features in contemplative neuroscience, and exhibit high predictive power for several types of meditation.We show the statistical significance of these network predictors - and their increased performance compared to popular spectral and non-linear features such as complexity or entropy - on data from highly skilled meditators, in a continuous setting applicable to real-time analysis and applications such as neurofeedback.