Meditators show more stable brain activity over time and a different pattern of voltage fluctuations, particularly in a central-parietal brain region, compared to non-meditators. Analyzing over 7,000 time-series features from resting EEG data of 49 meditators and 46 non-meditators, classifiers could identify meditators with 67% accuracy using features from one principal component. Meditators exhibited higher stationarity—more consistent statistical properties across short time segments—and an altered distribution of voltage values around the mean. Traditional band-power measures did not distinguish the groups. These findings suggest that meditation is associated with greater temporal stability in brain activity, which may relate to enhanced attentional stability.
Only 47 out of over 7,700 time-series features reliably distinguished wakefulness from anesthesia or sleep across all evaluation groups of flies. Most of these features were related to autocorrelation, indicating that signals during wakefulness remained correlated to their past for longer than during anesthesia or sleep. Features related to complexity or spectral power, often proposed as consciousness markers, failed to generalize across all datasets, though many showed consistent direction of effect. These results caution that many newly discovered potential consciousness markers may not generalize across datasets, and point to autocorrelation as a class of dynamical properties that does.
Over 7,000 time-series features extracted from resting EEG data distinguished meditators from non-meditators with 67% accuracy, but only in a central-parietal brain component. Meditators showed higher temporal stability (more consistent statistical properties across short time segments) and altered distribution of voltage values around the mean. Traditional band-power measures failed to differentiate the groups. The findings suggest that meditation is associated with greater attentional stability reflected in more stationary neural dynamics.