Particle Swarm Optimization Based Support Vector Machines in Motion Imagery

In order to improve the classification recognition rate, this paper carries out a theoretical analysis of the support vector machine (SVM), in order to find the optimal parameters C, g, the particle swarm algorithm optimized support vector machine (PSO-SVM) is introduced to carry out parameter optimization, and for the shortcomings of the PSO-SVM, the optimal classifier, i.e., Adaptive Particle Swarm Algorithm Optimized Support Vector Machine (APSO-SVM), is introduced and the extracted features are respectively inputted into the extracted features are fed into the optimal classifier APSO-SVM respectively. The final classification results show that the proposed feature fusion analysis and the introduced APSO-SVM can effectively classify MI EEG signals.