Macrostate and Microstate of EEG Spatio-Temporal Nonlinear Dynamics in Zen Meditation
Pei-Chen Lo, Tian Wu, Fang-Ling Liu
Journal of Behavioral and Brain Science 2017 DOI: 10.4236/jbbs.2017.713046 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Observational study Peer reviewed |
|---|---|
| Intervention | Zen meditation |
| Topics | Buddhism Meditation |
| Keywords | Ministate Electroencephalography Nonlinear system Artificial intelligence Pattern recognition psychology Similarity geometry |
| Citations | 4 |
| Key findings | Zen-meditation EEG exhibits more stationary and stronger interconnectivity among frontal midline regional neural oscillators compared to resting EEG, which drifts away from the midline to inferior brain regions. |
Abstract
Macrostate and microstate characteristics of interregional nonlinear interdependence of brain dynamics are investigated for Zen-meditation and normal resting EEG. Evaluation of nonlinear interdependence based on nonlinear dynamic theory and phase space reconstruction is employed in the 30-channel electroencephalographic (EEG) signals to characterize the functioning interactions among different local neuronal networks. This paper presents a new scheme for exploring the microstate and macrostate of interregional brain neural network interactivity. Nonlinear interdependence quantified by similarity index is applied to the phase trajectory reconstructed from multi-channel EEG. The microstate similarity-index matrix (miSIM) is evaluated every 5 millisecond. The miSIMs are classified by K-means clustering. The cluster center corresponds to the macrostate SIM (maSIM) evaluated by conventional scheme. Zen-meditation EEG exhibits rather stationary and stronger interconnectivity among frontal midline regional neural oscillators, whereas resting EEG appears to drift away more often from the midline and extend to the inferior brain regions.