Human errors are a root cause of majority of accidents occurring in the process industry. These errors are often a result of excessive workload on operators, especially during abnormal situations. Understanding and measurement of cognitive workload (overload), experienced by human operators while performing key safety critical tasks, is thus important to the understanding of human errors. Subjective measurements of workload are often not reliable and there is a need for physiological based parameters of workload. In this work, we propose a methodology to measure cognitive load of a control room operator in terms of a biomarker, specifically theta/alpha ratio, obtained from a single electrode EEG signal. Real-time detection of the biomarker can enable minimize errors and improve safety. © 2019 Elsevier B.V.