Measuring aviator workload using EEG: an individualized approach to workload manipulation

被引:0
|
作者
Feltman, Kathryn A. [1 ]
Vogl, Johnathan F. [1 ]
McAtee, Aaron [1 ,2 ]
Kelley, Amanda M. [1 ]
机构
[1] US Army Aeromed Res Lab, Ft Novosel, AL 36362 USA
[2] Goldbelt Inc, Herndon, VA USA
来源
关键词
workload; aviation; individualized; electroencephalograph; cognitive state; operator state monitoring; MENTAL WORKLOAD; PERFORMANCE;
D O I
10.3389/fnrgo.2024.1397586
中图分类号
TB18 [人体工程学];
学科分类号
1201 ;
摘要
Introduction: Measuring an operator's physiological state and using that data to predict future performance decrements has been an ongoing goal in many areas of transportation. Regarding Army aviation, the realization of such an endeavor could lead to the development of an adaptive automation system which adapts to the needs of the operator. However, reaching this end state requires the use of experimental scenarios similar to real-life settings in order to induce the state of interest that are able to account for individual differences in experience, exposure, and perception to workload manipulations. In the present study, we used an individualized approach to manipulating workload in order to account for individual differences in response to workload manipulations, while still providing an operationally relevant flight experience. Methods: Eight Army aviators participated in the study, where they completed two visits to the laboratory. The first visit served the purpose of identifying individual workload thresholds, with the second visit resulting in flights with individualized workload manipulations. EEG data was collected throughout both flights, along with subjective ratings of workload and flight performance. Results: Both EEG data and workload ratings suggested a high workload. Subjective ratings were higher during the high workload flight compared to the low workload flight (p < 0.001). Regarding EEG, frontal alpha (p = 0.04) and theta (p = 0.01) values were lower and a ratio of beta/(alpha+theta) (p = 0.02) were higher in the baseline flight scenario compared to the high workload scenario. Furthermore, the data were compared to that collected in previous studies which used a group-based approach to manipulating workload. Discussion: The individualized method demonstrated higher effect sizes in both EEG and subjective ratings, suggesting the use of this method may provide a more reliable way of producing high workload in aviators.
引用
收藏
页数:13
相关论文
共 50 条
  • [41] Measuring Perceived Mental Workload in Children
    Laurie-Rose, Cynthia
    Frey, Meredith
    Ennis, Aristi
    Zamary, Amanda
    AMERICAN JOURNAL OF PSYCHOLOGY, 2014, 127 (01): : 107 - 125
  • [42] Cognitive Workload Recognition Using EEG Signals and Machine Learning: A Review
    Zhou, Yueying
    Huang, Shuo
    Xu, Ziming
    Wang, Pengpai
    Wu, Xia
    Zhang, Daoqiang
    IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS, 2022, 14 (03) : 799 - 818
  • [43] Estimating cognitive workload using a commercial in-ear EEG headset
    Tremmel, Christoph
    Krusienski, Dean J.
    Schraefel, Mc
    JOURNAL OF NEURAL ENGINEERING, 2024, 21 (06)
  • [44] Reproducible machine learning research in mental workload classification using EEG
    Demirezen, Guliz
    Temizel, Tugba Taskaya
    Brouwer, Anne-Marie
    FRONTIERS IN NEUROERGONOMICS, 2024, 5
  • [45] Estimating Cognitive Workload in an Interactive Virtual Reality Environment Using EEG
    Tremmel, Christoph
    Herff, Christian
    Sato, Tetsuya
    Rechowicz, Krzysztof
    Yamani, Yusuke
    Krusienski, Dean J.
    FRONTIERS IN HUMAN NEUROSCIENCE, 2019, 13
  • [46] A Multimodal Approach Exploiting EEG to Investigate the Effects of VR Environment on Mental Workload
    Mondellini, Marta
    Pirovano, Ileana
    Colombo, Vera
    Arlati, Sara
    Sacco, Marco
    Rizzo, Giovanna
    Mastropietro, Alfonso
    INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION, 2024, 40 (20) : 6566 - 6578
  • [47] The Burden of Administrative Household Labor-Measuring Temporal Workload, Mental Workload, and Satisfaction
    Dethier, Erik
    Stevens, Gunnar
    Boden, Alexander
    SOCIAL SCIENCES-BASEL, 2024, 13 (08):
  • [48] Optimization of Workload Level Estimation Using Selection of EEG Channel Connectivity
    Ardian, Kevin
    Taya, Fumihiko
    Sun, Yu
    Bezerianos, Anastasios
    Ardian, Kevin
    Chen, Tan Kay
    2016 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2016, : 1985 - 1990
  • [49] EEG Based Mental Workload Estimation System
    Cebeci, Bora
    Akan, Aydin
    Sutcubasi, Bemis
    2020 28TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU), 2020,
  • [50] Mental Workload Detection Based on EEG Analysis
    Yauri, Jose
    Hernandez-Sabate, Aura
    Folch, Pau
    Gil, Debora
    ARTIFICIAL INTELLIGENCE RESEARCH AND DEVELOPMENT, 2021, 339 : 268 - 277