ArticlePloS one2021
Detection of mind wandering using EEG: Within and across individuals.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Temporal kinetics of brain state effects on visual perception.Scientific reports · 2026Article
- AI-Driven Hemodynamic Detection of Self-Induced Daydreaming With EMG-Based Physiological Triggers During Pre- and Post-Prandial States Using fNIRS and EGG.CNS neuroscience & therapeutics · 2026Article
- Capturing naturalistic thoughts using a precision experience sampling idiographic approach.Cognitive research: principles and implications · 2026Article
- Characterizing alterations in attention networks under high mental workload.Scientific reports · 2026Article
- Inferring mind wandering from perceptual decision making.Communications psychology · 2026Article
- Cognitive state monitoring for neuroadaptive information visualization.Frontiers in human neuroscience · 2026Review
- Detecting mind wandering via EEG and facial video features.Behavior research methods · 2025Article
- The role of working memory capacity on the neural mechanisms of mind wandering in drilling crews.Experimental brain research · 2025Article
- Electrophysiological signatures of ongoing thoughts during naturalistic behavior.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Brain-based predictive modeling of mind-wandering and involuntary thought: the idiographic approach.Technology, mind, and behavior · 2025Article
- EEG synchronization signatures for decoding attentional states during continuous force control.Frontiers in neuroscience · 2025Article
- Gaze-Based Detection of Thoughts across Naturalistic Tasks Using a PSO-Optimized Random Forest Algorithm.Bioengineering (Basel, Switzerland) · 2024Article
- EEG complexity measures for detecting mind wandering during video-based learning.Scientific reports · 2024Article
- Detecting cognitive traits and occupational proficiency using EEG and statistical inference.Scientific reports · 2024Article
- Individual variability in neural representations of mind-wandering.Network neuroscience (Cambridge, Mass.) · 2024Article
- Predicting attentional lapses using response time speed in continuous performance tasks.Frontiers in cognition · 2024Review
- Dynamic multilayer networks reveal mind wandering.Frontiers in neuroscience · 2024Article
- Generative Adversarial Network (GAN) for Simulating Electroencephalography.Brain topography · 2023Article
- Mind wandering state detection during video-based learning via EEG.Frontiers in human neuroscience · 2023Article
- Relationships between resting-state EEG functional networks organization and individual differences in mind wandering.Scientific reports · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mind wandering is often characterized by attention oriented away from an external task towards our internal, self-generated thoughts. This universal phenomenon has been linked to numerous disruptive functional outcomes, including performance errors and negative affect. Despite its prevalence and impact, studies to date have yet to identify robust behavioral signatures, making unobtrusive, yet reliable detection of mind wandering a difficult but important task for future applications. Here we examined whether electrophysiological measures can be used in machine learning models to accurately predict mind wandering states. We recorded scalp EEG from participants as they performed an auditory target detection task and self-reported whether they were on task or mind wandering. We successfully classified attention states both within (person-dependent) and across (person-independent) individuals using event-related potential (ERP) measures. Non-linear and linear machine learning models detected mind wandering above-chance within subjects: support vector machine (AUC = 0.715) and logistic regression (AUC = 0.635). Importantly, these models also generalized across subjects: support vector machine (AUC = 0.613) and logistic regression (AUC = 0.609), suggesting we can reliably predict a given individual's attention state based on ERP patterns observed in the group. This study is the first to demonstrate that machine learning models can generalize to "never-seen-before" individuals using electrophysiological measures, highlighting their potential for real-time prediction of covert attention states.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.