Cognitive Performance Assessment of UAS Sensor Operators via Neurophysiological Measures

• ¹ Drexel University, School of Biomedical Engineering, Science & Health Systems, United States
• ² Coventry University, School of Mechanical, Aerospace & Automotive Engineering, United Kingdom

Over the recent years, the information-processing load and decision-making demands have increased on aviation personnel including, pilots, unmanned aircraft systems (UAS) and air traffic controllers. Specifically, many UASs in addition to primary tasks, such as piloting, also require secondary tasks whose performance may be the main purpose of UAS flight mission. Whether an individual is responsible for the execution of primary and secondary, the cognitive demands assigned on the operator not only play a crucial role within the context of UAS operation, but also have the potential to offer important insight into training methodology to improve the success rate of the mission. These cognitive demands vary according to the role of the operator and the mission being executed. For example, in the case of surveillance and search missions, the pilot or an additional sensor operator (SO) is expected to operate a number of sensors such as a camera during different phases of flight. Assessing the neurophysiological measures under such varying cognitive demands can help evaluate expertise development and the cognitive capacities of the crew in ground control stations. Such an evaluation can provide additional performance metrics directly driven from brain based measures, which would be an important asset in maintaining safe and effective performance. Modern brain sensor techniques allow us to witness metabolic changes in brain states; such as those measured by Positron Emission Tomography (PET) and functional Magnetic Resonance Imaging (fMRI). These techniques, while affording the ability to better understand the physiological mechanisms for cognitive activity, have associated ergonomic constraints in terms of portability, invasiveness and complex configuration to reduce the effects of noise within naturalistic settings. However, recent advances in optical brain imaging techniques, in particular functional near infrared spectroscopy (fNIRS), have allowed portable application of monitoring brain activity of operators within their normal working environments. Specific activity within regions of the cerebral cortex are closely associated with higher level cognitive functioning such as decision-making, problem-solving, working memory and attentional focus. In a previous work, we reported results suggesting that fNIRS measures acquired from the dorsolateral prefrontal cortex (dlPFC) were associated with the development of scanning efficiency during SO training [1]. Here we extend these findings to explore skill acquisition and the development of neural measures associated with improvements in target identification task. Fifteen participants who had no prior UAS piloting experience, were placed in a Simlat’s C-STRT simulator training apparatus and underwent three sessions of experimentation. Within each session the participant was asked to navigate across six sub-areas, during which the participant was engaged in tasks such as route scanning, target detection and identification, and the tracking of identified targets. Continuous brain activity measures from the prefrontal cortex region were acquired via fNIRS and behavioral measures, such as scanned percentages, target detected and tracked times per subarea, were recorded from the simulator system. After classification of participants into high and low performers, significant differences were observed between groups during target search practice. Specifically, positive changes in oxygenation between the initial and final trial in the left dlPFC region, an area that has been reported to be associated with spatial working memory task [2], was observed in the high-performance group respective to low-performance group. An upward trend in number of targets detected and oxygenation was seen in high performers and a downward trend in the same measures was seen in low performers across trials, suggesting that those who were most actively engaged successfully accomplished the mission goal. These findings support previous results from route scanning task and UAS pilot training studies and indicate the benefits of applying neurophysiological measures in order to gain further objective insight into human cognitive performance [3].