Objective Functional near-infrared spectroscopy (fNIRS), a novel non-invasive technique for monitoring cerebral activity, can be integrated with upper limb rehabilitation robots to facilitate the real-time assessment of neurological rehabilitation outcomes. The rehabilitation robot is designed with 3 training modes: passive, active, and resistance. Among these, the resistance mode has been demonstrated to yield superior rehabilitative outcomes for patients with a certain level of muscle strength. The control modes in the resistance mode can be categorized into dynamic and static control. However, the effects of different control modes in the resistance mode on the motor function of patients with upper limb hemiplegia in stroke remain unclear. Furthermore, the effects of force, an important parameter of different control modes, on the activation of brain regions have rarely been reported. This study investigates the effects of dynamic and static resistance modes under varying resistance levels on cerebral functional alterations during motor rehabilitation in post-stroke patients.Methods A cohort of 20 stroke patients with upper limb dysfunction was enrolled in the study, completing preparatory adaptive training followed by 3 intensity-level tasks across 2 motor paradigms. The bilateral prefrontal cortices (PFC), bilateral primary motor cortices (M1), bilateral primary somatosensory cortices (S1), and bilateral premotor and supplementary motor cortices (PM) were examined in both the resting and motor training states. The lateralization index (LI), phase locking value (PLV), network metrics were employed to examine cortical activation patterns and topological properties of brain connectivity.Results The data indicated that both dynamic and static modes resulted in significantly greater activation of the contralateral M1 area and the ipsilateral PM area when compared to the resting state. The static patterns demonstrated a more pronounced activation in the contralateral M1 in comparison to the dynamic patterns. The results of brain network analysis revealed significant differences between the dynamic and resting states in the contralateral PFC area and contralateral M1 area (F=4.709, P=0.038), as well as in the contralateral PM area and ipsilateral M1 area (F=4.218, P=0.049). Moreover, the findings indicated a positive correlation between the activation of the M1 region and the increase in force in the dynamic mode, which was reversed in the static mode.Conclusion Both dynamic and static resistance training modes have been demonstrated to activate the corresponding brain functional regions. Dynamic resistance modes elicit greater oxygen changes and connectivity to the region of interest (ROI) than static resistance modes. Furthermore, the effects of increasing force differ between the two modes. In patients who have suffered a stroke, dynamic modes may have a more pronounced effect on the activation of exercise-related functional brain regions.