Building a Drone Operator Digital Twin using a Brain-Computer Interface for Emotion Recognition

Drones are often associated with data collection operations and monitoring services. Thus, they are particularly relevant for performing high-risk tasks remotely, allowing the operator to execute them safely. These operations require a high level of attention and focus from the operator; however, humans are prone to stress and distractions, which negatively impacts the control he delivers. Brain-computer interfaces can be a way to enable the analysis of the emotional states of the drone operator. In this paper, we propose a decision-making system that evaluates the emotional state of the drone operator to allow commands to be sent to the drone. By developing a digital twin of the operator capable of classifying the emotional states at both cognitive and visual levels, the system can decide whether the operator is in a proper emotional state to control the drone. Necessary information is computed and forwarded to the drone through a ROS2 client node. Initial evaluation results indicate that the digital twin can detect emotions and mood swings in a real-time setting and that the system may provide a reliable and secure way of validating drone commands through the mind.