ArticleBehavior research methods2025
Detecting mind wandering via EEG and facial video features.
Article in Behavior research methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Neurocognitive Mechanism of Perceptual Errors in Radiologists: Results of Pilot Studies.Neuroscience insights · 2026Article
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3 authors.
Funding
Abstract
purposeMind wandering (MW), a common cognitive phenomenon marked by a shift of attention away from the task at hand, poses significant challenges in online educational settings. This study aims to advance MW detection by developing a classification scheme that leverages multimodal data, including electroencephalograph (EEG) signals and facial video recorded using a commercial off-the-shelf webcam. Additionally, this study provides an in-depth analysis of feature contributions and explores the correlation between self-reported introspective confidence, mental state stability, and classification performance, offering deeper insights into MW detection.
methodsData were collected from 26 college students during a video-based learning task, interspersed with modified experience sampling probes. To enhance the sample size and address autocorrelation in EEG signals, a probe-based sample extraction method was applied. MW classification was performed using a random forest algorithm, with features derived from both EEG signals and facial video recordings. Model performance was evaluated using within-participant tenfold cross-validation and leave-one-participant-out (LOPO) cross-validation.
resultsThe combination of EEG and video features yielded better performance (AUC = 0.68 for within-participant; AUC = 0.56 for LOPO) compared to using EEG or video alone. Individual differences significantly influenced performance, with a 10% increase in AUC observed when training data included samples from the evaluated individual in augmented LOPO cross-validation. Introspective confidence levels positively correlated with classification performance, while mental state temporal stability was associated with improved cross-participant performance. Additionally, the size of the training set positively correlated with cross-participant performance when combining EEG and video features.
conclusionThese findings underscore the potential of multimodal approaches for MW detection and highlight the importance of individual differences and data diversity in classifier training. The study provides actionable insights into improving MW detection systems for real-world applications in educational settings.
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