title: [Research on magnetoencephalography-brain computer interface based on the PCA and LDA data reduction].
abstract: The magnetoencephalography (MEG) can be used as a control signal for brain computer interface (BCI). The BCI also includes the pattern information of the direction of hand movement. In the MEG signal classification, the feature extraction based on signal processing and linear classification is usually used. But the recognition rate has been difficult to improve. In the present paper, a principal component analysis (PCA) and linear discriminant analysis (LDA) method has been proposed for the feature extraction, and the non-linear nearest neighbor classification is introduced for the classifier. The confusion matrix is analyzed based on the results. The experimental results show that the PCA + LDA method is effective in the analysis of multi-channel MEG signals, improves the recognition rate to the extent of the average recognition rate 55.7%, which is better than the recognition rate 46.9% in the BCI competition IV.
authors.affiliation: College of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China. wjj@ysu.edu.cn
authors.forename: Jinjia
authors.initials: J
authors.lastname: Wang
authors.forename: Lina
authors.initials: L
authors.lastname: Zhou
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology*; D016002:Discriminant Analysis; D004569:Electroencephalography; D006225:Hand / Q000502:physiology*; D006801:Humans; D015225:Magnetoencephalography / Q000379:methods*; D009068:Movement / Q000502:physiology; D025341:Principal Component Analysis; D012815:Signal Processing, Computer-Assisted; D014584:User-Computer Interface