Uncovering a stability signature of brain dynamics associated with meditation experience using massive time-series feature extraction

• Published in: Neural networks 2024-03, Vol.171, p.171-185
• Main Authors: Bailey, Neil W, Fulcher, Ben D., Caldwell, Bridget, Hill, Aron T, Fitzgibbon, Bernadette, van Dijk, Hanneke, Fitzgerald, Paul B
• Format: Article
• Language: English
• Subjects: Electroencephalography; Hctsa; Machine learning; Massive feature extraction; Meditation; Time-series analysis
• ISSN: 0893-6080; 1879-2782
• DOI: 10.1016/j.neunet.2023.12.007

•Massive time-series feature set detects features differing in meditator EEG activity.•Higher temporal stability is a key signature of meditators’ EEG activity.•Meditators’ EEG activity shows an altered distributional shape of voltage values.•Traditional frequency band power did not distinguish meditators and non-meditators. 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.