Neuroadaptive Training via fNIRS in Flight Simulators
Training to master a new skill often takes a lot of time, effort, and financial resources, particularly when the desired skill is complex, time sensitive, or high pressure where lives may be at risk. Professions such as aircraft pilots, surgeons, and other mission-critical operators that fall under...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-03-01
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| Series: | Frontiers in Neuroergonomics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnrgo.2022.820523/full |
| _version_ | 1819159249039654912 |
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| author | Jesse A. Mark Amanda E. Kraft Matthias D. Ziegler Hasan Ayaz Hasan Ayaz Hasan Ayaz Hasan Ayaz Hasan Ayaz |
| author_facet | Jesse A. Mark Amanda E. Kraft Matthias D. Ziegler Hasan Ayaz Hasan Ayaz Hasan Ayaz Hasan Ayaz Hasan Ayaz |
| author_sort | Jesse A. Mark |
| collection | DOAJ |
| description | Training to master a new skill often takes a lot of time, effort, and financial resources, particularly when the desired skill is complex, time sensitive, or high pressure where lives may be at risk. Professions such as aircraft pilots, surgeons, and other mission-critical operators that fall under this umbrella require extensive domain-specific dedicated training to enable learners to meet real-world demands. In this study, we describe a novel neuroadaptive training protocol to enhance learning speed and efficiency using a neuroimaging-based cognitive workload measurement system in a flight simulator. We used functional near-infrared spectroscopy (fNIRS), which is a wearable, mobile, non-invasive neuroimaging modality that can capture localized hemodynamic response and has been used extensively to monitor the anterior prefrontal cortex to estimate cognitive workload. The training protocol included four sessions over 2 weeks and utilized realistic piloting tasks with up to nine levels of difficulty. Learners started at the lowest level and their progress adapted based on either behavioral performance and fNIRS measures combined (neuroadaptive) or performance measures alone (control). Participants in the neuroadaptive group were found to have significantly more efficient training, reaching higher levels of difficulty or significantly improved performance depending on the task, and showing consistent patterns of hemodynamic-derived workload in the dorsolateral prefrontal cortex. The results of this study suggest that a neuroadaptive personalized training protocol using non-invasive neuroimaging is able to enhance learning of new tasks. Finally, we outline here potential avenues for further optimization of this fNIRS based neuroadaptive training approach. As fNIRS mobile neuroimaging is becoming more practical and accessible, the approaches developed here can be applied in the real world in scale. |
| first_indexed | 2024-12-22T16:37:33Z |
| format | Article |
| id | doaj.art-419112f8c1f248919d8ed2f7b8bf9576 |
| institution | Directory Open Access Journal |
| issn | 2673-6195 |
| language | English |
| last_indexed | 2024-12-22T16:37:33Z |
| publishDate | 2022-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroergonomics |
| spelling | doaj.art-419112f8c1f248919d8ed2f7b8bf95762022-12-21T18:19:54ZengFrontiers Media S.A.Frontiers in Neuroergonomics2673-61952022-03-01310.3389/fnrgo.2022.820523820523Neuroadaptive Training via fNIRS in Flight SimulatorsJesse A. Mark0Amanda E. Kraft1Matthias D. Ziegler2Hasan Ayaz3Hasan Ayaz4Hasan Ayaz5Hasan Ayaz6Hasan Ayaz7School of Biomedical Engineering, Science, and Health Systems, Drexel University, Philadelphia, PA, United StatesAdvanced Technology Laboratories, Lockheed Martin, Arlington, VA, United StatesAdvanced Technology Laboratories, Lockheed Martin, Arlington, VA, United StatesSchool of Biomedical Engineering, Science, and Health Systems, Drexel University, Philadelphia, PA, United StatesDepartment of Psychological and Brain Sciences, College of Arts and Sciences, Drexel University, Philadelphia, PA, United StatesDrexel Solutions Institute, Drexel University, Philadelphia, PA, United StatesDepartment of Family and Community Health, University of Pennsylvania, Philadelphia, PA, United StatesCenter for Injury Research and Prevention, Children's Hospital of Philadelphia, Philadelphia, PA, United StatesTraining to master a new skill often takes a lot of time, effort, and financial resources, particularly when the desired skill is complex, time sensitive, or high pressure where lives may be at risk. Professions such as aircraft pilots, surgeons, and other mission-critical operators that fall under this umbrella require extensive domain-specific dedicated training to enable learners to meet real-world demands. In this study, we describe a novel neuroadaptive training protocol to enhance learning speed and efficiency using a neuroimaging-based cognitive workload measurement system in a flight simulator. We used functional near-infrared spectroscopy (fNIRS), which is a wearable, mobile, non-invasive neuroimaging modality that can capture localized hemodynamic response and has been used extensively to monitor the anterior prefrontal cortex to estimate cognitive workload. The training protocol included four sessions over 2 weeks and utilized realistic piloting tasks with up to nine levels of difficulty. Learners started at the lowest level and their progress adapted based on either behavioral performance and fNIRS measures combined (neuroadaptive) or performance measures alone (control). Participants in the neuroadaptive group were found to have significantly more efficient training, reaching higher levels of difficulty or significantly improved performance depending on the task, and showing consistent patterns of hemodynamic-derived workload in the dorsolateral prefrontal cortex. The results of this study suggest that a neuroadaptive personalized training protocol using non-invasive neuroimaging is able to enhance learning of new tasks. Finally, we outline here potential avenues for further optimization of this fNIRS based neuroadaptive training approach. As fNIRS mobile neuroimaging is becoming more practical and accessible, the approaches developed here can be applied in the real world in scale.https://www.frontiersin.org/articles/10.3389/fnrgo.2022.820523/fullneuroergonomicsfNIRSneuroadaptiveprefrontal cortexneurofeedbacklearning |
| spellingShingle | Jesse A. Mark Amanda E. Kraft Matthias D. Ziegler Hasan Ayaz Hasan Ayaz Hasan Ayaz Hasan Ayaz Hasan Ayaz Neuroadaptive Training via fNIRS in Flight Simulators Frontiers in Neuroergonomics neuroergonomics fNIRS neuroadaptive prefrontal cortex neurofeedback learning |
| title | Neuroadaptive Training via fNIRS in Flight Simulators |
| title_full | Neuroadaptive Training via fNIRS in Flight Simulators |
| title_fullStr | Neuroadaptive Training via fNIRS in Flight Simulators |
| title_full_unstemmed | Neuroadaptive Training via fNIRS in Flight Simulators |
| title_short | Neuroadaptive Training via fNIRS in Flight Simulators |
| title_sort | neuroadaptive training via fnirs in flight simulators |
| topic | neuroergonomics fNIRS neuroadaptive prefrontal cortex neurofeedback learning |
| url | https://www.frontiersin.org/articles/10.3389/fnrgo.2022.820523/full |
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