MENTAL WORKLOAD MEASUREMENT IN CONTROL SYSTEM ENGINEERS USING EEG INDICES

Abstract

In control system environments, there are high levels of sustained attention, decision making, problem solving, as well as time-sensitive and high-stakes considerations. Cognitive functions that induce high levels of mental workload on engineers, and may contribute to human error, fatigue, and compromised system safety, if left unmonitored. Therefore, understanding mental workload as well as quantifying mental workload will aid in creating adaptive interfaces and improving overall operational efficiency. This study documented the use of electroencephalography (EEG) as a non-invasive neurophysiological measure of mental workload in control system engineers. EEG measures provide insight that are real-time, objective indicators of cognitive demands because the electroencephalogram measures through changes in brain activity expressed differently by varying task loads or intensities. In this study, participants completed simulated control system tasks that simulated routine and emergencies. Continuous EEG data were recorded throughout the demonstration, and data collected during emergencieswere the focus of the analysis. In addition to event-level analysis of demand/workload through a reanalysis of the EEG data, key indicators used to estimate participants' workload were frontal theta power and parietal alpha suppression while engaging in control system tasks, as these measures correlate well with mental effort and attentional regulation. Results found EEG workload patterns that distinguished cognitive load according to varying levels of task complexity and task duration; with more complex tasks and tasks of longer duration created higher workload in terms of theta activity and lower workload in terms of alpha power. Overall, the findings of this study highlight the potential of EEG-based monitoring in real-time in operational settings to detect mental overload before performance decline. The study highlights critical implications for enhancing human reliability, preventing fatigue-related errors, and informing the development of intelligent workload-aware systems in complex operational environments.