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Pilot training requires significant resources, both material and human. Immersive virtual reality is a good way to reduce costs and get around the lack of resources availability. However, the effectiveness of virtual flight simulation has not yet been fully assessed, in particular, using physiological measures. In this AQ1 study, 10 pilots performed standard traffic patterns on both real aircraft (DR400) and its virtual simulation (in head-mounted device and motion platform).We used subjective measures through questionnaires of immersion, presence, and ability to control the aircraft, and objective measures using heart rate, and heart rate variability. The results showed that the pilots were able to fully control the aircraft. Points to improve include updating the hardware (better display resolution and hand tracking) and the simulator dynamics for modelling ground effect. During AQ2 the real experience, the overall heart rate (HR) was higher (+20 bpm on average), and the heart rate variability (HRV) was lower compared to the virtual experience. The flight phases in both virtual and real flights induced similar cardiac responses with more mental efforts during take-off and landing compared to the downwind phase. Overall, our findings indicate that virtual flight reproduces real flight and can be used for pilot training. However, replacing pilot training with exclusively virtual flight hours seems utopian at this point.

The present study proposes a novel concept of neuroadaptive technology, namely a dual passive-reactive Brain-Computer Interface (BCI), that enables bi-directional interaction between humans and machines. We have implemented such a system in a realistic flight simulator using the NextMind classification algorithms and framework to decode pilots’ intention (reactive BCI) and to infer their level of attention (passive BCI). Twelve pilots used the reactive BCI to perform checklists along with an anti-collision radar monitoring task that was supervised by the passive BCI. The latter simulated an automatic avoidance maneuver when it detected that pilots missed an incoming collision. The reactive BCI reached 100% classification accuracy with a mean reaction time of 1.6s when exclusively performing the checklist task. Accuracy was up to 98.5% with a mean reaction time of 2.5s when pilots also had to fly the aircraft and monitor the anti-collision radar. The passive BCI achieved a F1 − score of 0.94. This first demonstration shows the potential of a dual BCI to improve human-machine teaming which could be applied to a variety of applications.

The inability of pilots to detect unexpected changes in the environment (e.g., auditory alarms) is a critical problem in aeronautics. The lack of response to alarms is not thought to be a perceptual/attentional issue, but rather that pilots choose to ignore such warnings due to cognitive biases. In the current paper we consider an alternative explanation, by extending the phenomenon of inattentional deafness to aeronautics. Fourteen pilots equipped with an eye tracker and an electrocardiogram performed landings in a flight simulator. During the critical landing, an auditory landing gear alarm was triggered while the volunteers also faced a windshear. Eight out of 14 pilots did not report the occurrence of the critical alarm during the debriefing. Interestingly, all but one of these 'deaf' pilots failed to perform the adequate go-around behavior. These findings establish inattentional deafness as a cognitive phenomenon that is critical for air safety. Keywords: Driver distraction; Language: en