Dynamic target selection is one of the most basic interactive tasks in modern interaction interfaces. There are a variety of assistive techniques, but the design and parameters of these techniques are largely based on experimental data and cannot be adjusted according to the users' current state. In order to solve this problem, a brain-computer interface assisted dynamic target selection technique based on two assumptions of cognitive load and difficulty perception in this study is proposed, which uses the functional near-infrared spectroscopy (fNIRS) signals to cognitive load perception of users and adjusts the parameters of target selection techniques in real time. This technique can provide personalized assistance to different users and be applicable to different scenarios, user status and task difficulty. The proposed hypothesis through a set of experiments is verified, and brain-computer assisted dynamic target selection technique constructed based on this assumption is better than both the auxiliary and fixed auxiliary technologies. Specifically, the selection error rate is reduced by 20.55% and 12.09% respectively, and the completion time is reduced by 998.35 ms and 208.67 ms respectively.