Study on the impact of incentive mechanisms on the work efficiency of tower crane drivers based on EEG

The tower crane operators play a crucial role in construction operations, and their work efficiency directly affects the success of the construction projects. However, the heavy and repetitive nature of their tasks often leads to operator fatigue, which in turn compromises both performance and safety. This study investigates the impact of incentive mechanisms on the work efficiency of tower crane operators by analyzing the electroencephalogram (EEG) signals of the operators collected. The multifractal detrended fluctuation analysis (MF-DFA) method is employed to examine the Hurst index range and multifractal spectrum width of the EEG signals under two incentive modes: a reward-punishment incentive mode and a no reward-punishment mode. The research results show that there are significant differences between the two incentive modes (p < 0.05). The Hurst index range and multifractal spectrum width of the EEG signals under the reward-punishment mode are greater than those under the no reward-punishment mode. Furthermore, during different working stages, the lifting efficiency of prefabricated components is consistently higher in the reward-punishment incentive mode. This indicates that drivers under the incentive mode have less mental fatigue and higher work efficiency. Additionally, in the incentive mode, the lifting efficiency of prefabricated components is significantly higher, demonstrating the practical benefits of such incentives in improving work performance. From this study, it can be concluded that the reward-punishment incentive mechanism effectively improves the work efficiency of tower crane operators by positively influencing their cognitive state. Based on the EEG signals, the impact of incentive mechanisms on human performance can be better understood, and valuable insights can be provided for optimizing the safety and operational efficiency of construction sites.