Target Detection System Based on P300 Brain-Machine Interface: Effect of Frame Rate on Brain Wave and Classification System

Target detection system based on P300 brain-computer interface realizes the organic fusion of machine intelligence and biological intelligence and opens up new ideas for target detection technology. In order to study the influence mechanism of the frame rate on the EEG characteristics of the system, we tested the frame rate of five subjects using six different images. Experimental results show that the frame rate of image projection is positively correlated with the latency of P300 and negatively correlated with the intensity of P300 signal. The HDCA classification algorithm was used to extract and classify the P300 signal extracted at different frame rates. The classification results showed that the P300 classification accuracy was high under low frame rate stimulation. Considering the working efficiency of the system, it is proposed to adopt 7-8Hz as the target working frequency of the system.