Meditation experience was associated with higher temporal stability in resting EEG, allowing classification of meditators from non-meditators with 67% accuracy (pFDR=0.007).
Observational (n=95)
Does massive time-series feature extraction of EEG data improve the identification of brain activity related to meditation compared to traditional band-power measures?
Massive time-series feature extraction of EEG data reveals that meditators exhibit higher temporal stability and altered distributional shape of values, outperforming traditional band-power measures in distinguishing meditators from non-meditators.
Effect estimate: Accuracy 67%
p-value: p=0.007
Previous research has examined resting electroencephalographic (EEG) data to explore brain activity related to meditation. However, previous research has mostly examined power in different frequency bands. The practical objective of this study was to comprehensively test whether other types of time-series analysis methods are better suited to characterize brain activity related to meditation. To achieve this, we compared >7000 time-series features of the EEG signal to comprehensively characterize brain activity differences in meditators, using many measures that are novel in meditation research. Eyes-closed resting-state EEG data from 49 meditators and 46 non-meditators was decomposed into the top eight principal components (PCs). We extracted 7381 time-series features from each PC and each participant and used them to train classification algorithms to identify meditators. Highly differentiating individual features from successful classifiers were analysed in detail. Only the third PC (which had a central-parietal maximum) showed above-chance classification accuracy (67%, pFDR = 0.007), for which 405 features significantly distinguished meditators (all pFDR 0.05). Our novel analysis approach suggests the key signatures of meditators’ brain activity are higher temporal stability and a distribution of time-series values suggestive of longer, larger, or more frequent non-outlying voltage deviations from the mean within the third PC of their EEG data. The higher temporal stability observed in this EEG component might underpin the higher attentional stability associated with meditation. The novel time-series properties identified here have considerable potential for future exploration in meditation research and the analysis of neural dynamics more broadly.
Bailey et al. (Wed,) conducted a observational in Meditation experience (n=95). Meditation experience vs. Non-meditators was evaluated on Classification accuracy of meditators vs non-meditators using EEG time-series features (Accuracy 67%, p=0.007). Meditation experience was associated with higher temporal stability in resting EEG, allowing classification of meditators from non-meditators with 67% accuracy (pFDR=0.007).