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

摘要

In this paper, an analysis method of electroencephalograph (EEG) based on the motor imagery is proposed. Butterworth band-pass filter and artifact removal technique are combined to extract the feature of frequency band of ERD/ERS. Common spatial pattern (CSP) is used to extract feature vector. Support Vector Machine (SVM) is used for signal classification of motor imagery EEG. To improve classification performance, the parameters of SVM are optimized by a new bio-inspired method called Magnetic Bacteria Optimization Algorithm (MBOA). Experimental results show that MBOA has good performance on the problem of SVM optimization and obtain good classification results on EEG signals.