Dynamic assessment of control room operator's cognitive workload using Electroencephalography (EEG)

In modern plants with high levels of automation, acquiring an adequate mental model of the process has become a challenge for operators. Studies indicate that sub-optimal decisions occur when there is a mismatch between the demands of the process and the human's capability. This mismatch leads to high cognitive workload in human operators, often a precursor for poor performance. Recently, researchers in various safety critical domains (aviation, driving, marine, NPP, etc.) have started to explore the use of physiological measurements from humans to understand their cognitive workload and its effect. In this work, we evaluate the potential of EEG to measure cognitive workload of human operators in chemical process control room. We propose a single dry electrode EEG based methodology for identifying the similarities and mismatch between the operators’ mental model of the process and the actual process behaviour during abnormal situations. Our results reveal that S^Ɵ(ω), the power spectral density of theta (ɵ) waves (frequency range 4–7 Hz) in the EEG signal has the potential to identify such mismatches. Results indicate that S^Ɵ(ω) is positively correlated with workload and hence can be used for assessing the cognitive workload of operators in process industries.