The motor imagery brain-computer interface has been widely used in the field of brain-computer interconnection due to its greater autonomy and flexibility. Compared with other paradigms, the classification accuracy rate is low, which limits its development. In this paper, two feature analysis methods of time-frequency atlas and brain topography were used to analyze the EEG signals of upper limb motor imagery, and the filter bank co-space (FBCSP) feature extraction algorithm was used to analyze the characteristics of upper limb motor imagery. The signal data is extracted with features, and then the extraction results are divided into three types: support vector machine (SVM) algorithm, K-Nearest Neighbor (K-Nearest Neighbor) algorithm, and back propagation (BP) neural network. According to the classification algorithm, the research results found that the average classification accuracy of the SVM algorithm, KNN algorithm and BP neural network algorithm applied to the upper limb motor imagery brain-computer interface system was 76.45%, 74.55%, and 81.70%, respectively. Compared with the SVM, the BP neural network algorithm The algorithm and KNN algorithm are 5.25% and 7.15% higher in the classification accuracy of the upper limb motor imagery task respectively, and the classification accuracy obtained after the t test has a very significant statistical difference, and the ROC curve and AUC value were used to detect Compared with the SVM algorithm and the KNN algorithm, the AUC value of the BP neural network is also increased by 0.1226 and 0.1285 respectively, indicating that the BP neural network classification algorithm is more suitable for the upper limb motor imagery brain-computer interface system than the SVM algorithm and the KNN algorithm. , improve the classification accuracy of the system, and promote the development process of the practical application of upper limb motor imagery EEG signals.