文献类型：期刊文献

英文题名：Spatial-Temporal Discriminant Analysis for ERP-Based Brain-Computer Interface

作者：Zhang, Yu[1,2];Zhou, Guoxu[2];Zhao, Qibin[2];Jin, Jing[1];Wang, Xingyu[1];Cichocki, Andrzej[2,3]

机构：[1]E China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China;[2]RIKEN Brain Sci Inst, Lab Adv Brain Signal Proc, Wako, Saitama 3510198, Japan;[3]Polish Acad Sci, Syst Res Inst, PL-00901 Warsaw, Poland

年份：2013

卷号：21

期号：2

起止页码：233

外文期刊名：IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING

收录：;EI(收录号：20131216130550);WOS:【SCI-EXPANDED(收录号:WOS:000316264100010)】；

基金：This work was supported in part by the Nation Nature Science Foundation of China under Grant 61074113, Grant 61203127, Grant 61103122, and Grant 61202155, in part by the Shanghai Leading Academic Discipline Project B504, and in part by the Fundamental Research Funds for the Central Universities under Grant WH1114038.

语种：英文

外文关键词：Brain-computer interface (BCI); electroencephalogram (EEG); event-related potential (ERP); linear discriminant analysis (LDA); spatial-temporal discriminant analysis (STDA)

摘要：Linear discriminant analysis (LDA) has been widely adopted to classify event-related potential (ERP) in brain-computer interface (BCI). Good classification performance of the ERP-based BCI usually requires sufficient data recordings for effective training of the LDA classifier, and hence a long system calibration time which however may depress the system practicability and cause the users resistance to the BCI system. In this study, we introduce a spatial-temporal discriminant analysis (STDA) to ERP classification. As a multiway extension of the LDA, the STDA method tries tomaximize the discriminant information between target and nontarget classes through finding two projection matrices from spatial and temporal dimensions collaboratively, which reduces effectively the feature dimensionality in the discriminant analysis, and hence decreases significantly the number of required training samples. The proposed STDA method was validated with dataset II of the BCI Competition III and dataset recorded from our own experiments, and compared to the state-of-the-art algorithms for ERP classification. Online experiments were additionally implemented for the validation. The superior classification performance in using few training samples shows that the STDA is effective to reduce the system calibration time and improve the classification accuracy, thereby enhancing the practicability of ERP-based BCI.