Brain-computer interfaces (BCI) based on steady-state visual evoked potentials (SSVEP) have attracted great interest because of their higher signal-to-noise ratio, less training, and faster information transfer. However, the existing signal recognition methods for SSVEP do not fully pay attention to the important role of signal phase characteristics in the recognition process. Therefore, an improved method based on extended Canonical Cor-relation Analysis (eCCA) is proposed. The phase parameters are added from the stimulus paradigm encoded by joint frequency phase modulation to the reference signal constructed from the training data of the subjects to achieve phase constraints on eCCA, thereby improving the recognition performance of the eCCA method for SSVEP signals, and transmit the collected signals to the robotic arm system to achieve control of the robotic arm. In order to verify the effectiveness and advantages of the proposed method, this paper evaluated the method using SSVEP signals from 35 subjects. The research shows that the proposed algorithm improves the average recognition rate of SSVEP signals to 82.76%, and the information transmission rate to 116.18 bits/min, which is superior to TRCA and traditional eCAA-based methods in terms of information transmission speed and accu-racy, and has better stability.