The neural mass model is a self-oscillation network composed of two neural populations. In this study, we use the fieldprogrammable gate array (FPGA) device to implement the neural mass model and the hardware implementation results are exactly the same as the MATLAB simulation results. The study reveals that dynamical characteristics of the neural population implemented on FPGA can meet the real-Time computational requirements. Besides, we propose a control method of the robotic arm based on the oscillation dynamics of the network. For the implementation results of FPGA is real-Time, it can be used to realize the robotic control. A closed-loop control system is realized by inputting the error signals of robotic arm into the neural network model and obtaining the feedback signal to arm joint for error elimination. The results show that the control method based on the neural mass model can quickly and effectively eliminate the angle errors.