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

Autoři: Kiret Dhindsa aff001;  Anita Acai aff001;  Natalie Wagner aff001;  Dan Bosynak aff002;  Stephen Kelly aff001;  Mohit Bhandari aff001;  Brad Petrisor aff001;  Ranil R. Sonnadara aff001
Působiště autorů: Department of Surgery, McMaster University, Hamilton, Ontario, Canada aff001;  Research and High-Performance Computing Support, McMaster University, Hamilton, Ontario, Canada aff002;  Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada aff003;  Department of Psychology, Neuroscience, & Behaviour, McMaster University, Hamilton, Ontario, Canada aff004;  LIVELab, McMaster University, Hamilton, Ontario, Canada aff005;  Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada aff006;  Department of Surgery, University of Toronto, Toronto, Ontario, Canada aff007
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
doi: 10.1371/journal.pone.0222276


Neural correlates of mind wandering

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.

Mind wandering detection

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.

Klíčová slova:

Social sciences – Sociology – Education – Lectures – Research and analysis methods – Bioassays and physiological analysis – Electrophysiological techniques – Brain electrophysiology – Electroencephalography – Imaging techniques – Simulation and modeling – Biology and life sciences – Physiology – Electrophysiology – Neurophysiology – Neuroscience – Brain mapping – Functional magnetic resonance imaging – Neuroimaging – Cognitive science – Cognitive psychology – Attention – Cognition – Neural networks – Psychology – Medicine and health sciences – Clinical medicine – Clinical neurophysiology – Diagnostic medicine – Diagnostic radiology – Magnetic resonance imaging – Radiology and imaging – Computer and information sciences – Artificial intelligence – Machine learning – Physical sciences – Mathematics – Applied mathematics – Algorithms – Machine learning algorithms


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