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

Jessica Morton, Aleksandra Zheleva (UGent) , Bram Van Acker (UGent) , Wouter Durnez (UGent) , Pieter Vanneste, Charlotte Larmuseau, Jonas De Bruyne (UGent) , Annelies Raes, Frederik Cornillie, Jelle Saldien (UGent) , et al.
(2022) APPLIED ERGONOMICS. 102.
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Abstract
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.
Keywords
Physical Therapy, Sports Therapy and Rehabilitation, Engineering (miscellaneous), Safety, Risk, Reliability and Quality, Human Factors and Ergonomics, Industrial setting, Assembly task, Cognitive workload, Cognitive ergonomics, Overload, EEG, EOG, MENTAL WORKLOAD, WORKING-MEMORY, WIRELESS EEG, PERFORMANCE, TASK, ALPHA, LOAD, OSCILLATIONS, OPERATORS, INDEXES

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MLA
Morton, Jessica, et al. “Danger, High Voltage! Using EEG and EOG Measurements for Cognitive Overload Detection in a Simulated Industrial Context.” APPLIED ERGONOMICS, vol. 102, 2022, doi:10.1016/j.apergo.2022.103763.
APA
Morton, J., Zheleva, A., Van Acker, B., Durnez, W., Vanneste, P., Larmuseau, C., … Bombeke, K. (2022). Danger, high voltage! Using EEG and EOG measurements for cognitive overload detection in a simulated industrial context. APPLIED ERGONOMICS, 102. https://doi.org/10.1016/j.apergo.2022.103763
Chicago author-date
Morton, Jessica, Aleksandra Zheleva, Bram Van Acker, Wouter Durnez, Pieter Vanneste, Charlotte Larmuseau, Jonas De Bruyne, et al. 2022. “Danger, High Voltage! Using EEG and EOG Measurements for Cognitive Overload Detection in a Simulated Industrial Context.” APPLIED ERGONOMICS 102. https://doi.org/10.1016/j.apergo.2022.103763.
Chicago author-date (all authors)
Morton, Jessica, Aleksandra Zheleva, Bram Van Acker, Wouter Durnez, Pieter Vanneste, Charlotte Larmuseau, Jonas De Bruyne, Annelies Raes, Frederik Cornillie, Jelle Saldien, Lieven De Marez, and Klaas Bombeke. 2022. “Danger, High Voltage! Using EEG and EOG Measurements for Cognitive Overload Detection in a Simulated Industrial Context.” APPLIED ERGONOMICS 102. doi:10.1016/j.apergo.2022.103763.
Vancouver
1.
Morton J, Zheleva A, Van Acker B, Durnez W, Vanneste P, Larmuseau C, et al. Danger, high voltage! Using EEG and EOG measurements for cognitive overload detection in a simulated industrial context. APPLIED ERGONOMICS. 2022;102.
IEEE
[1]
J. Morton et al., “Danger, high voltage! Using EEG and EOG measurements for cognitive overload detection in a simulated industrial context,” APPLIED ERGONOMICS, vol. 102, 2022.
@article{8749165,
  abstract     = {{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.}},
  articleno    = {{103763}},
  author       = {{Morton, Jessica and Zheleva, Aleksandra and Van Acker, Bram and Durnez, Wouter and Vanneste, Pieter and Larmuseau, Charlotte and De Bruyne, Jonas and Raes, Annelies and Cornillie, Frederik and Saldien, Jelle and De Marez, Lieven and Bombeke, Klaas}},
  issn         = {{0003-6870}},
  journal      = {{APPLIED ERGONOMICS}},
  keywords     = {{Physical Therapy,Sports Therapy and Rehabilitation,Engineering (miscellaneous),Safety,Risk,Reliability and Quality,Human Factors and Ergonomics,Industrial setting,Assembly task,Cognitive workload,Cognitive ergonomics,Overload,EEG,EOG,MENTAL WORKLOAD,WORKING-MEMORY,WIRELESS EEG,PERFORMANCE,TASK,ALPHA,LOAD,OSCILLATIONS,OPERATORS,INDEXES}},
  language     = {{eng}},
  pages        = {{9}},
  title        = {{Danger, high voltage! Using EEG and EOG measurements for cognitive overload detection in a simulated industrial context}},
  url          = {{http://doi.org/10.1016/j.apergo.2022.103763}},
  volume       = {{102}},
  year         = {{2022}},
}
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