Danger, high voltage! Using EEG and EOG measurements for cognitive overload detection in a simulated industrial context

Industrial settings will be characterized by far-reaching production automation brought about by advancements in robotics and artificial intelligence. As a consequence, human assembly workers will need to adapt quickly to new and more complex assembly procedures, which are most likely to increase co...

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Bibliographic Details
Published inApplied ergonomics Vol. 102; p. 103763
Main Authors Morton, Jessica, Zheleva, Aleksandra, Van Acker, Bram B., Durnez, Wouter, Vanneste, Pieter, Larmuseau, Charlotte, De Bruyne, Jonas, Raes, Annelies, Cornillie, Frederik, Saldien, Jelle, De Marez, Lieven, Bombeke, Klaas
Format Journal Article
LanguageEnglish
Published England Elsevier Ltd 01.07.2022
Subjects
Artificial Intelligence
Assembly task
Cognition
Cognitive ergonomics
Cognitive workload
EEG
Electroencephalography
Electrooculography - methods
EOG
Eye Movements
Humans
Industrial setting
Overload
Industrial setting
Cognitive ergonomics
Overload
EEG
Cognitive workload
EOG
Assembly task
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Summary:Industrial settings will be characterized by far-reaching production automation brought about by advancements in robotics and artificial intelligence. As a consequence, human assembly workers will need to adapt quickly to new and more complex assembly procedures, which are most likely to increase cognitive workload, or potentially induce overload. Measurement and optimization protocols need to be developed in order to be able to monitor workers’ cognitive load. Previous studies have used electroencephalographic (EEG, measuring brain activity) and electrooculographic (EOG, measuring eye movements) signals, using basic computer-based static tasks and without creating an experience of overload. In this study, EEG and EOG data was collected of 46 participants performing an ecologically valid assembly task while inducing three levels of cognitive load (low, high and overload). The lower individual alpha frequency (IAF) was identified as a promising marker for discriminating between different levels of cognitive load and overload. •Cognitive (over)load studied in the context of a personalised assembly task.•Multidimensional approach with self-report, performance, and physiological measures.•Individual alpha frequency & blink rate revealed distinct levels of cognitive load.
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ISSN:0003-6870
1872-9126
DOI:10.1016/j.apergo.2022.103763