Use of dry electrode electroencephalography (EEG) to monitor pilot workload and distraction based on P300 responses to an auditory oddball task

Zara Gibson, Joseph Butterfield, Matthew Roger, Brian Murphy, Adelaide Marzano

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Abstract

This study aims to examine whether dry electrode EEG can detect and show changes in the P300, in a movement and noise polluted flight simulator environment with a view to using it for workload and distraction monitoring. Twenty participants completed take-off, cruise and landing flight phases in a flight simulator alongside an auditory oddball task. Dry EEG sensors monitored the participants’ brain activity throughout the task and P300 responses were extracted from the resulting data. Results show that dry EEG can extract P300 responses as participants register oddball tone stimuli. The method can indicate workload for each condition based on the outputs from the EEG electrodes; landing (M = 287.5) and take-off (M = 484.6) procedures were more difficult than cruising (M = 636.6). With the differences between cruising and landing being statistically significant (p = .001). Outcomes correlate with participant NASA-TLX scores of workload that report landing to be the most difficult.
Original languageEnglish
Title of host publicationAdvances in Neuroergonomics and Cognitive Engineering
Subtitle of host publicationAHFE 2018
PublisherSpringer
Pages14-26
Number of pages13
Volume775
ISBN (Electronic)978-3-319-94866-9
ISBN (Print)978-3-319-94865-2
DOIs
Publication statusE-pub ahead of print - 28 Jun 2018

Publication series

NameAdvances in Intelligent Systems and Computing
PublisherSpringer
Volume775
ISSN (Print)2194-5357

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Keywords

  • Pain
  • Flight simulation
  • Workload
  • Dry EEG
  • Human Factors

Cite this

Gibson, Z., Joseph Butterfield, Roger, M., Murphy, B., & Marzano, A. (2018). Use of dry electrode electroencephalography (EEG) to monitor pilot workload and distraction based on P300 responses to an auditory oddball task. In Advances in Neuroergonomics and Cognitive Engineering: AHFE 2018 (Vol. 775, pp. 14-26). (Advances in Intelligent Systems and Computing; Vol. 775). Springer. https://doi.org/10.1007/978-3-319-94866-9_2