Motor Imagery Electroencephalograph Classification Based on Optimized Support Vector Machine by Magnetic Bacteria Optimization Algorithm

In this paper, an optimized support vector machine (SVM) based on a new bio-inspired method called magnetic bacteria optimization algorithm method is proposed to construct a high performance classifier for motor imagery electroencephalograph based brain-computer interface (BCI). Butterworth band-pass filter and artifact removal technique are combined to extract the feature of frequency band of the ERD/ERS. Common spatial pattern is used to extract the feature vector which are put into the classifier later. The optimization mechanism involves kernel parameters setting in the SVM training procedure, which significantly influences the classification accuracy. Our novel approach aims to optimize the penalty factor parameter C and kernel parameter g of the SVM. The experimental results on the BCI Competition IV dataset II-a clearly present the effectiveness of the proposed method outperforming other competing methods in the literature such as genetic algorithm, particle swarm algorithm, artificial bee colony, biogeography based optimization.