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PLoS One. 2019 Sep 12;14(9):e0222276. doi: 10.1371/journal.pone.0222276. eCollection 2019.

Individualized pattern recognition for detecting mind wandering from EEG during live lectures.

Author information

Department of Surgery, McMaster University, Hamilton, Ontario, Canada.
Research and High-Performance Computing Support, McMaster University, Hamilton, Ontario, Canada.
Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada.
Department of Psychology, Neuroscience, & Behaviour, McMaster University, Hamilton, Ontario, Canada.
LIVELab, McMaster University, Hamilton, Ontario, Canada.
Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada.
Department of Surgery, University of Toronto, Toronto, Ontario, Canada.



The ability to detect mind wandering as it occurs is an important step towards improving our understanding of this phenomenon and studying its effects on learning and performance. Current detection methods typically rely on observable behaviour in laboratory settings, which do not capture the underlying neural processes and may not translate well into real-world settings. We address both of these issues by recording electroencephalography (EEG) simultaneously from 15 participants during live lectures on research in orthopedic surgery. We performed traditional group-level analysis and found neural correlates of mind wandering during live lectures that are similar to those found in some laboratory studies, including a decrease in occipitoparietal alpha power and frontal, temporal, and occipital beta power. However, individual-level analysis of these same data revealed that patterns of brain activity associated with mind wandering were more broadly distributed and highly individualized than revealed in the group-level analysis.


To apply these findings to mind wandering detection, we used a data-driven method known as common spatial patterns to discover scalp topologies for each individual that reflects their differences in brain activity when mind wandering versus attending to lectures. This approach avoids reliance on known neural correlates primarily established through group-level statistics. Using this method for individual-level machine learning of mind wandering from EEG, we were able to achieve an average detection accuracy of 80-83%.


Modelling mind wandering at the individual level may reveal important details about its neural correlates that are not reflected when using traditional observational and statistical methods. Using machine learning techniques for this purpose can provide new insight into the varieties of neural activity involved in mind wandering, while also enabling real-time detection of mind wandering in naturalistic settings.

Conflict of interest statement

The authors have declared that no competing interests exist in relation to this work.

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