Abstract: The feature extraction, classification and recognition of electroencephalogram signals of motor imagination are the difficult problems faced by the current Brain Computer Interface (BCI) technology. Aiming at this problem, this paper proposes a classification method of motor imaging EEG signals combining Principal Component Analysis (PCA) and Particle Swarm Optimization optimized-Support Vector Machine (PSO-SVM). Firstly, PCA is used to reduce the dimension of the collected high-dimensional electroencephalogram signal, eliminating the noise components and extracting the feature vectors reflecting the different characteristics of three-dimensional EEG signals. Then SVM is used to classify the feature vectors. In view of the problem that the SVM classification performance is greatly affected by the kernel parameters, the global optimization ability of PSO algorithm is used to optimize the SVM classification performance so as to improve the SVM classification performance. Finally, the Graz data used in the BCI competition is used for experiments. The results show that the proposed PCA fusion PSO-SVM method can obtain 95.3% classification performance, and has a high application prospect.

Key words: brain computer interface, PCA, PSO, SVM, feature classification