Real-Time Mental Workload Estimation Using EEG

Aneta Kartali, Milica M. Janković, Ivan Gligorijević, Pavle Mijović, Bogdan Mijović, Maria Chiara Leva

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Tracking mental workload in real-time during worker’s performance is a challenge, as it requires the worker to report during the task execution. Moreover, it is thus based on subjective experience. Neuroergonomics tackles this issue, by employing neurophysiological metrics to obtain objective, real-time information. We measured mental workload (MWL) derived from electroencephalography (EEG) signals, for subjects engaged in a simulated computer-based airplane-landing task. To test this metric, we calculated the degree of correlation between measured MWL and observable variables associated to task complexity. In the two settings of the experiment, we used a 24-channel full-cap EEG system and the novel mobile EEG headphone device. The latter allows seamless integration of the EEG acquisition system in a possible real-world setup scenario. Obtained results reveal significant correlation between the EEG derived MWL metric and the two objective task complexity metrics: the number of airplanes on the screen subjects had to control, as well as the number of actions performed by the subject during the task in both setups. Therefore, this work represents a proof of concept for using the proposed systems for reliable real-time mental workload tracking.

Original languageEnglish
Title of host publicationHuman Mental Workload
Subtitle of host publicationModels and Applications - 3rd International Symposium, H-WORKLOAD 2019, Proceedings
EditorsLuca Longo, Maria Chiara Leva
PublisherSpringer
Pages20-34
Number of pages15
ISBN (Print)9783030324223
DOIs
Publication statusPublished - 2019
Event3rd International Symposium on Human Mental Workload: Models and Applications, H-WORKLOAD 2019 - Rome, Italy
Duration: 14 Nov 201915 Nov 2019

Publication series

NameCommunications in Computer and Information Science
Volume1107
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference3rd International Symposium on Human Mental Workload: Models and Applications, H-WORKLOAD 2019
Country/TerritoryItaly
CityRome
Period14/11/1915/11/19

Keywords

  • EEG
  • Mental workload
  • Neuroergonomics

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