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Facial electromyography (fEMG) is an electromyographic measurement technique that has primarily been used as a tool for measuring affect, but previous experiments suggest that it also has the potential to help quantify cognitiveworkload. In the current study, two task-irrelevant facial muscles, corrugator supercilli and lateral frontalis, were monitored in real-time to determine whether they were sensitive to workload changes in a remotely piloted aircraft (RPA) task environment. Real-time signal processing techniques were applied to derive the median amplitude and zero-crossing rate from windowed fEMG data. Statistical analysis of these features determined that both muscles were sensitive to variations in specific workload manipulations. This research suggests that real-time fEMG features extracted from the aforementionedmuscles possess the potential to serve as, or contribute to, anindex of cognitiveworkload. Future work aims to refine fEMG data collection techniques to produce a more responsive and representative measure suitable for workload assessment.