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 language | English |
---|---|
Publication status | Published - 21 Jul 2018 |
Event | 9th International Conference on Applied Human Factors and Ergonomics - Loews Sapphire Falls Resort, Orlando, United States Duration: 21 Jul 2018 → 25 Jul 2018 Conference number: 9 http://www.ahfe2018.org/submission.html |
Conference
Conference | 9th International Conference on Applied Human Factors and Ergonomics |
---|---|
Abbreviated title | AHFE 2018 |
Country/Territory | United States |
City | Orlando |
Period | 21/07/2018 → 25/07/2018 |
Internet address |
Keywords
- P300, Flight Simulation, workload, dry EEG, Human Factors, NASA TLX
Fingerprint
Dive into the research topics of 'Use of Dry Electrode Electroencephalography (EEG) to Monitor Pilot Workload and Distraction Based on P300 Responses to an Auditory Oddball Task'. Together they form a unique fingerprint.Student theses
-
Virtual reality and psychological tools in the assessment of operator performance in complex human-machine interactions
Gibson, Z. (Author), Butterfield, J. (Supervisor), Rodger, M. (Supervisor), Murphy, B. (Supervisor) & Marzano, A. (Supervisor), Dec 2019Student thesis: Doctoral Thesis › Doctor of Philosophy
File