Cognitive Workload and Interface Performance: A Neuroergonomic Comparison of VR, AR, and Traditional Drone Control Systems

Abstract: As small Unmanned Aerial Systems (sUAS) become critical in defense, emergency response, public safety, and aerial media production, understanding how control interfaces shape cognitive workload becomes increasingly important. This study examined how latency and joystick sensitivity influence human–machine synchrony across different pilot skill levels. Using a within-subjects design and real-time EEG during standardized flight tasks, the investigation observed how operators adapted to varying feedback delays and controller responsiveness. Asynchrony consistently elevated theta activity, reduced alpha power, and destabilized beta rhythms. Novice pilots reached these asynchrony thresholds rapidly, exhibiting reactive, correction-driven control loops, while advanced pilots maintained predictive control. Across participants, low-latency, medium-sensitivity settings formed a narrow stability corridor that preserved the most temporal coherence between user intention and system response. These findings characterize the neurophysiological signatures of efficient human–machine coupling and lay the groundwork for intelligent sUAS interfaces that adapt their behavior to support human predictive control in real time.

Keywords: Neuroergonomics, Human–Computer Interaction, Virtual and Augmented Reality, Drone Control Systems, Cognitive Workload, Interface Modality

DOI: 10.54941/ahfe1006902

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