Electroencephalography (EEG) Signal-Based Mental Workload Detection for Intelligent Workstation Machine Cognitive Task - Wu, Xiaoli; Yan, Biao; Han, Weiyi; Zhang, Lan; Liu, Xiao; Wu, Yuhan; Zhang, Xinyue; Wang, Zhiyu; Zou, Yuhang (International Journal of Industrial Engineering: Theory, Applications and Practice, 2025)
The intelligent workstation serves as a cornerstone in the advancement of intelligent and digital manufacturing systems, with operators' roles increasingly shifting from physical labor to cognitive effort. To examine the effect of cognitive task difficulty on mental workload (MWL), operational tasks of low, medium, and high difficulty were designed as experimental conditions. The National Aeronautics and Space Administration Task Load Index (NASA-TLX), task performance metrics, and electroencephalographic (EEG) data were utilized to compare differences across the three levels. The results showed that, under high task difficulty, the average whole-brain power spectral density (PSD) in the Theta, Alpha, and High Beta frequency bands was significantly elevated among the 20 participants. Further electrode-level analysis revealed that the sensitivity and discriminative capacity of EEG signals varied across frequency bands and electrode sites. These EEG-based indicators demonstrate strong potential as neurophysiological biomarkers for differentiating levels of MWL and detecting operator overload in intelligent workstation contexts.