eeg-based evaluation of mental workload in a simulated industrial human-robot interaction task

Background: the rapid advancement of robotics and artificial intelligence is poised to revolutionize industrial settings through widespread automation. this study investigates the impact of robotic assistance on human operator mental workload (mwl) within a simulated industrial environment. utilizing electroencephalography (eeg) to measure changes in alpha and theta band power, we aim to identify the cognitive challenges associated with human-robot collaboration (hrc) and inform the design of safer and more efficient collaborative systems. objectives: the main objective of the current study was to assess the mwl in a simulated industrial human-robot interaction (hri) task. methods: the eeg data were collected from 17 participants (aged 25 - 35 years) using a 64-channel system while they engaged in an ecologically valid robotic task that induced three distinct levels of cognitive load: low, medium, and high. subsequent analysis focused on eeg power within the alpha and theta frequency bands, employing repeated-measures anova to assess the impact of cognitive load on brain activity. results: a repeated-measures anova revealed significant changes in eeg power across different task difficulty levels. the theta and alpha bands in f3, f4, and fz, as well as the alpha, beta, and gamma bands in p3, p4, and pz, emerged as promising indicators for differentiating between varying levels of cognitive load in human-robot tasks. conclusions: electroencephalography spectral power, particularly within the alpha and theta frequency bands, is a reliable indicator of human mwl. these frequency bands exhibit dynamic changes in response to fluctuating cognitive demands, especially in human-robotic interaction tasks.