Abstract: Current manufacturing is increasingly characterized by multi-variety and small-batch production, which places higher demands on the dynamic adaptability of operators' learning capabilities. Traditional learning curve models are often inadequate for accurately predicting task times, as they overlook dynamic fluctuations in learning rates and lack quantitative physiological feedback. In this study, a dynamic learning rate quantification model (EEG-MOD) is proposed by integrating electroencephalogram (EEG) technology with motion study theory. Firstly, flashlight assembly experiments and an orthogonal experimental design were conducted, validating the significant effects of motion complexity, attention frequency, and temporal factors on EEG characteristics and learning rates. Secondly, through experiments, it was shown that motion factors had the most significant impact on EEG variations (F=4.77, p=0.030). A multivariate regression-based EEG-MOD model was developed. The EEG-MOD model constructed using multiple regression achieved a goodness-of-fit of R^2 =0.89. Finally, case studies demonstrated that this model can dynamically adjust the traditional learning rate during the wiring process of low-voltage distribution panels, with the adjustment range generally falling between 0.3% and 8.2%, thereby better reflecting the dynamic changes in the learning rate. This study provides theoretical support for dynamic learning rate monitoring and labor-hour prediction in manufacturing, enhancing the reliability of production modeling and simulation software.