EEG-Based Evaluation of Mental Workload in a Simulated Industrial Human-Robot Interaction Task

Fazli, Babak; Sajadi, Seyed Saman; Jafari, Amir Homayoun; Garosi, Ehsan; Hosseinzadeh, Soheila; Zakerian, Seyed Abolfazl; Azam, Kamal · 2025 · Crossref

DOI: 10.5812/healthscope-158096

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Summary

This study investigates the impact of robotic assistance on human operator mental workload (MWL) within a simulated industrial human-robot interaction (HRI) environment. Motivated by the rapid integration of robotics in industrial settings and the need to understand cognitive challenges in collaborative systems, the research aims to identify reliable physiological indicators of MWL. Specifically, it evaluates whether electroencephalography (EEG) spectral power, particularly in alpha and theta bands, can effectively differentiate between varying levels of cognitive demand during complex HRI tasks. The experimental design involved 17 male participants aged 25–35 years, recruited from Tehran University of Medical Sciences. Participants performed a simulated robotic assembly task using a five-degree-of-freedom robotic arm controlled via joysticks. To ensure ecological validity, the task required simultaneous motor control and cognitive processing, including visual-spatial manipulation and working memory retention. Cognitive workload was manipulated across three levels—low, medium, and high—by varying the number of stimuli to be memorized (2, 5, and 7 items, respectively) and introducing Stroop-like interference effects. EEG data were collected using a 64-channel dry electrode system at a 500 Hz sampling rate. Data preprocessing included bandpass filtering, artifact removal via independent component analysis, and epoch extraction. Statistical analysis employed repeated-measures ANOVA to assess changes in absolute power across theta, alpha, beta, and gamma frequency bands at frontal and parietal electrodes. The results demonstrated significant changes in EEG power corresponding to task difficulty. Frontal theta power (channels F3, F4, Fz) increased significantly as cognitive load rose from low to high levels, with large effect sizes (partial eta squared > 0.96). Conversely, parietal alpha power (channels P3, P4, Pz) exhibited a significant decreasing trend with increased workload. Additionally, parietal beta and gamma band powers showed significant increases across the three load conditions. Post-hoc analyses confirmed significant differences between all pairwise comparisons of load levels for these specific frequency bands and regions. The study concludes that EEG spectral power, particularly frontal theta and parietal alpha, beta, and gamma bands, serves as a reliable indicator of MWL in industrial HRI tasks. The findings suggest that theta power reflects the brain’s effort to manage information under demanding conditions, while alpha power correlates negatively with cognitive load. These results support the use of EEG for objective MWL assessment, offering a valuable tool for optimizing human-robot interface design and enhancing workplace safety and efficiency in collaborative industrial environments.

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Topics

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• neuro workload indices
• workload measurement
• mental demand

Information type

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• Empirical Findings: physiological data, self report data