Generalized Deep Learning EEG Models for Cross-Participant and Cross-Task Detection of the Vigilance Decrement in Sustained Attention Tasks

Tasks which require sustained attention over a lengthy period of time have been a focal point of cognitive fatigue research for decades, with these tasks including air traffic control, watchkeeping, baggage inspection, and many others. Recent research into physiological markers of mental fatigue ind...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 21; no. 16; p. 5617
Main Authors: Kamrud, Alexander, Borghetti, Brett, Schubert Kabban, Christine, Miller, Michael
Format: Journal Article
Language:English
Published: Basel MDPI AG 20.08.2021
MDPI
Subjects:
Accuracy
Aircraft
Artificial intelligence
Automation
Classification
cross-participant
Datasets
deep learning
EEG
Electroencephalography
Human performance
Machine learning
mental fatigue
sustained attention
Taxonomy
vigilance decrement
ISSN:1424-8220, 1424-8220
Online Access:Get full text
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Summary:Tasks which require sustained attention over a lengthy period of time have been a focal point of cognitive fatigue research for decades, with these tasks including air traffic control, watchkeeping, baggage inspection, and many others. Recent research into physiological markers of mental fatigue indicate that markers exist which extend across all individuals and all types of vigilance tasks. This suggests that it would be possible to build an EEG model which detects these markers and the subsequent vigilance decrement in any task (i.e., a task-generic model) and in any person (i.e., a cross-participant model). However, thus far, no task-generic EEG cross-participant model has been built or tested. In this research, we explored creation and application of a task-generic EEG cross-participant model for detection of the vigilance decrement in an unseen task and unseen individuals. We utilized three different models to investigate this capability: a multi-layer perceptron neural network (MLPNN) which employed spectral features extracted from the five traditional EEG frequency bands, a temporal convolutional network (TCN), and a TCN autoencoder (TCN-AE), with these two TCN models being time-domain based, i.e., using raw EEG time-series voltage values. The MLPNN and TCN models both achieved accuracy greater than random chance (50%), with the MLPNN performing best with a 7-fold CV balanced accuracy of 64% (95% CI: 0.59, 0.69) and validation accuracies greater than random chance for 9 of the 14 participants. This finding demonstrates that it is possible to classify a vigilance decrement using EEG, even with EEG from an unseen individual and unseen task.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s21165617