Nonlinear EEG signatures of mind wandering during breath focus meditation

Lu, Yiqing; Rodriguez-Larios, Julio · 2022 · Crossref

DOI: 10.1101/2022.03.27.485924

archive: archived pipeline: cataloged verified

Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)

Summary

This study investigates the neural correlates of mind wandering during breath focus meditation in novice practitioners, specifically addressing inconsistencies found in previous research using linear EEG metrics. While prior studies have attempted to identify neural signatures of distraction using oscillatory power (e.g., alpha and theta bands), results have been inconsistent. Given that the brain is a nonlinear, chaotic system, the authors hypothesize that nonlinear EEG complexity metrics may provide a more robust characterization of the transition between focused attention and mind wandering. The research aims to determine if these nonlinear signatures can reliably distinguish between breath focus and mind wandering states, potentially facilitating the development of EEG-based neurofeedback protocols for meditation training. The researchers analyzed a publicly available EEG dataset from 25 novice participants who performed breath focus meditation while undergoing EEG recording. Using an experience sampling paradigm, participants were randomly interrupted by a bell sound every 20 to 60 seconds and asked to report whether they were focusing on their breath or mind wandering. EEG data were preprocessed using artifact subspace reconstruction and independent component analysis to remove noise and eye movements. The study calculated EEG complexity using three distinct nonlinear algorithms: Higuchi’s fractal dimension (HFD), Lempel-Ziv complexity (LZC), and Sample entropy (SampEn). Statistical comparisons between mind wandering and breath focus conditions were conducted using cluster-based non-parametric randomization tests, with additional analyses controlling for unequal trial counts and participant drowsiness. The results demonstrated that EEG complexity was significantly reduced during mind wandering compared to breath focus states across all three metrics. HFD analysis revealed a widespread decrease in complexity across electrodes during mind wandering. LZC analysis showed significant decreases in parietal and right frontocentral regions, with a non-significant trend in the left frontal area. Similarly, SampEn indicated lower complexity during mind wandering. These findings remained consistent even when trial counts were matched between conditions, suggesting the results were not biased by unequal sample sizes. The reduction in complexity suggests that mind wandering is associated with more regular, less random brain activity compared to the focused attention state. The study concludes that nonlinear EEG complexity metrics are effective tools for disentangling mind wandering from breath focus states in novice meditators. Unlike linear spectral measures, which have yielded inconsistent results, complexity metrics provide a clear distinction between these cognitive states. This finding has significant implications for the field of contemplative neuroscience, suggesting that EEG complexity could serve as a reliable biomarker for real-time neurofeedback systems. Such systems could help novice practitioners detect and reduce mind wandering, thereby facilitating more effective meditation practice. The low computational cost of the selected algorithms further supports their potential application in real-time clinical or training settings.

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed.

Summary generated by qwen3.6-27b-nvidia on 2026-08-10; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• neuro workload indices