Research on microwave signal recognition of motor imagination brain activity based on support vector machine

In this paper, the original data of microwave signals of motor imagination brain activity is obtained through experiments. The entropy eigenvalues of the original data are extracted, and then PCA is used to reduce the dimension and optimize the characteristic data. Finally, SVM classifier and SVM-BETTER classification model are used to recognize brain activity signals. The experimental results show that the average recognition accuracy of the SVM-BETTER classifier is 0.8737, which is higher than most current research methods, It can accurately recognize the brain activity information of motor imagination.