Optimizing Human-Machine Interfaces for Neuroergonomics: Cognitive Workload and Performance in sUAS Operations

The growing prevalence of small Unmanned Aerial Systems (sUAS) or Drones across industries, particularly, aerial photography and surveying, results in a need for a deeper understanding of how control interface design impacts operator cognitive workload and performance. This study evaluates the effects of gyroscopic and traditional joystick-based control systems on the operator’s cognitive workload as well as their mission performance under diverse environmental conditions. Participants perform standardized sUAS tasks while real-time electroencephalography (EEG) monitoring tracks cognitive workload via theta and alpha wave activity. Results indicate that gyroscopic controls, though intuitive, increase cognitive workload under high stress, whereas joystick controls provide more stability. Performance metrics show greater consistency with traditional controls, especially in demanding conditions. Insights from this study inform ergonomic improvements, tailored training, and real-time physiological monitoring for adaptive systems. By integrating neuroergonomics with human-machine interface design, this research advances sUAS usability, optimizing operator performance and safety in dynamic environments.