Neuroadaptive Training via fNIRS in Flight Simulators

Jesse A Mark; Amanda E Kraft; Matthias D Ziegler; Hasan Ayaz

doi:10.3389/fnrgo.2022.820523

Neuroadaptive Training via fNIRS in Flight Simulators

Front Neuroergon. 2022 Mar 30:3:820523. doi: 10.3389/fnrgo.2022.820523. eCollection 2022.

Authors

Jesse A Mark¹, Amanda E Kraft², Matthias D Ziegler², Hasan Ayaz^{1

3

4

5

6}

Affiliations

¹ School of Biomedical Engineering, Science, and Health Systems, Drexel University, Philadelphia, PA, United States.
² Advanced Technology Laboratories, Lockheed Martin, Arlington, VA, United States.
³ Department of Psychological and Brain Sciences, College of Arts and Sciences, Drexel University, Philadelphia, PA, United States.
⁴ Drexel Solutions Institute, Drexel University, Philadelphia, PA, United States.
⁵ Department of Family and Community Health, University of Pennsylvania, Philadelphia, PA, United States.
⁶ Center for Injury Research and Prevention, Children's Hospital of Philadelphia, Philadelphia, PA, United States.

Abstract

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.

Keywords: adaptive training; aviation; fNIRS; learning; neuroadaptive; neuroergonomics; neurofeedback; prefrontal cortex.