文献类型：期刊文献

英文题名：Efficient Spatial Filters Enhance SSVEP Target Recognition Based on Task-Related Component Analysis

作者：Wang, Zhiqiang[1];Jin, Jing[1];Xu, Ren[2];Liu, Chang[1];Wang, Xingyu[1];Cichocki, Andrzej[3,4]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Guger Technol OG, A-8020 Graz, Austria;[3]Skolkovo Inst Sci & Technol, Moscow 121205, Russia;[4]Nicolaus Copernicus Univ, Dept Appl Comp Sci, PL-87100 Torun, Poland

年份：2022

卷号：14

期号：3

起止页码：1119

外文期刊名：IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS

收录：;EI(收录号：20213010675543);WOS:【SCI-EXPANDED(收录号:WOS:000852243600032)】；

基金：This work was supported in part by the National Key Research and Development Program under Grant 2017YFB13003002; in part by the National Natural Science Foundation of China under Grant 61573142 and Grant 61773164; in part by the Program of Introducing Talents of Discipline to Universities through the 111 Project under Grant B17017; in part by the ShuGuang Project supported by the Shanghai Municipal Education Commission and the Shanghai Education Development Foundation under Grant 19SG25; in part by the Ministry of Education and Science of the Russian Federation under Grant 14.756.31.0001; and in part by the Polish National Science Center under Grant UMO-2016/20/W/NZ4/00354.

语种：英文

外文关键词：Spatial filters; Training data; Task analysis; Correlation; Training; Target recognition; Correlation coefficient; 2-D linear discriminant analysis (2DLDA); 2-D locality preserving projections (2DLPP); brain-computer interface (BCI); steady-state visual evoked potential (SSVEP); task-related component analysis (TRCA)

摘要：Task-related component analysis (TRCA) has been applied successfully in the recently popular steady-state visual evoked potential (SSVEP) target recognition methods. However, a spatial filter is trained for each class in TRCA, and the training of each filter uses only the training data of the corresponding class. Therefore, the information between classes is ignored in the training process, which leads to classification inefficiency. Aiming at solving this defect in TRCA, we proposed a 2-D locality preserving projections (2DLPP) method and a 2-D linear discriminant analysis (2DLDA) method based on the 2-Norm form of Pearson's correlation coefficient. The 2DLPP and 2DLDA methods can simultaneously use the samples of all categories to train the spatial filters so that these two methods can make use of the information between classes to some extent. We also showed that the 2DLPP method and the 2DLDA method performed significantly better than the multiset canonical correlation analysis (MsetCCA), extended CCA (eCCA), and TRCA methods with two public data sets. Therefore, the proposed methods based on 2DLPP or 2DLDA can make more efficient use of sample information and have a great potential for SSVEP target recognition.