Mental Workload Assessment for UAV Traffic Control Using Consumer-Grade BCI Equipment

Abstract

Abstract. The increasing popularity of unmanned aerial vehicles (UAVs) in critical applications makes supervisory systems based on the presence of human in the control loop of crucial importance. In UAVtraffic monitoring scenarios, where human operators are responsible for managing drones, systems flexibly supporting different levels of autonomy are needed to assist them when critical conditions occur. The assessment of UAV controllers' performance thus their mental workload may be used to discriminate the level and type of automation required. The aim of this paper is to build a mental-workload prediction model based on UAV operators' cognitive demand to support the design of an adjustable autonomy supervisory system. A classification and validation procedure was performed to both categorize the cognitive workload measured by ElectroEncephaloGram signals and evaluate the obtained patterns from the point of view of accuracy. Then, a user study was carried out to identify critical workload conditions by evaluating operators' performance in accomplishing the assigned tasks. Results obtained in this study provided precious indications for guiding next developments in the field.

EEG based Emotion Prediction based on Audio Visual Stimulation using En-FS and SEn-G model

“…It is necessary to identify and recognize the emotions in computer systems that people communicate with, and to facilitate contact between humans and machines [2]. Emotion recognition has many applications, for example, knowing the mental states of a person, we can adapt a user interface and thus providing an effective man-machine interaction [3], evaluating the performance of an operator carrying out a task [4], human reliability analysis [5], etc. Many researchers are researching detecting and classifying various types of emotion by classifiers using signal processing, machine learning [6]- [9], deep learning [10]- [12] technolo-gies.…”