Multi-class classification of control room operators' cognitive workload using the fusion of eye-tracking and electroencephalography

Abstract

Chemical process industries are hazard intensive. Most industrial accidents occur today due to human error. For safe and efficient operation, it is therefore critical to ensure optimal operator performance. With the advent of Industry 4.0 and concomitant digitalization, the role of operators has become cognitively challenging. Therefore, it is imperative to assess the cognitive performance of operators. One of the major constructs to understand cognitive performance is the cognitive workload. An increase in cognitive workload often leads to degradation in performance. Traditional assessment techniques fail to capture cognitive aspects of performance. Recently, researchers in various domains such as aviation, driving, marine, and nuclear power have started to utilize physiological measures to gauge the cognitive workload of their operators. In our previous works, we have used electroencephalography (EEG) and eye-tracking separately to assess the cognitive workload of operators in the process industries. In contrast, in this paper, we explore the benefits of their fusion in classifying process industry control room operators’ workload into low, medium, and high classes while they tackle abnormal situations. The methodology employs the fusion of metrics derived from pupil, gaze, and EEG data to train a decision tree-based model for workload classification. Our results reveal that fusion leads to an increase in classification accuracy of upto 22 %. The work has the potential to identify the expertise level of operators and hence, can be critical in ensuring their optimal performance.