Macrostate and Microstate of EEG Spatio-Temporal Nonlinear Dynamics in Zen Meditation

During Zen meditation, the brain's frontal midline regions show more stable and stronger neural interconnectivity compared to normal resting states. Using 30-channel EEG recordings, the study analyzed nonlinear interdependence between brain regions by reconstructing phase trajectories and computing similarity-index matrices every 5 milliseconds. These microstate matrices were classified into macrostates via K-means clustering. Zen-meditation EEG exhibited stationary and robust coupling among frontal midline neural oscillators, while resting EEG showed connectivity that drifted more frequently from the midline and extended to inferior brain regions.

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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.