文献类型：期刊文献

英文题名：Robust Similarity Measurement Based on a Novel Time Filter for SSVEPs Detection

作者：Jin, Jing[1];Wang, Zhiqiang[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 Skoltech, Moscow 121205, Russia;[4]Nicolaus Copernicus Univ UMK, Dept Appl Comp Sci, PL-87100 Torun, Poland

年份：2023

卷号：34

期号：8

起止页码：4096

外文期刊名：IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS

收录：;EI(收录号：20214311082619);WOS:【SCI-EXPANDED(收录号:WOS:000732086900001)】；

基金：This work was supported in part by the National Key Research and Development Program under Grant 2017YFB13003002, Grant 2018YFC2002300, and Grant 2018YFC2002301; in part by the Grant National Natural Science Foundation of China under Grant 61573142 and Grant 61773164; in part 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：Time measurement; Training; Task analysis; Correlation; Visualization; Steady-state; Linear programming; Brain-computer interface(BCI); similarity measurement; steady-state visual evoked potential (SSVEP); task-related component analysis (TRCA); time filter

摘要：The steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) has received extensive attention in research for the less training time, excellent recognition performance, and high information translate rate. At present, most of the powerful SSVEPs detection methods are similarity measurements based on spatial filters and Pearson's correlation coefficient. Among them, the task-related component analysis (TRCA)-based method and its variant, the ensemble TRCA (eTRCA)-based method, are two methods with high performance and great potential. However, they have a defect, that is, they can only suppress certain kinds of noise, but not more general noises. To solve this problem, a novel time filter was designed by introducing the temporally local weighting into the objective function of the TRCA-based method and using the singular value decomposition. Based on this, the time filter and (e)TRCA-based similarity measurement methods were proposed, which can perform a robust similarity measure to enhance the detection ability of SSVEPs. A benchmark dataset recorded from 35 subjects was used to evaluate the proposed methods and compare them with the (e)TRCA-based methods. The results indicated that the proposed methods performed significantly better than the (e)TRCA-based methods. Therefore, it is believed that the proposed time filter and the similarity measurement methods have promising potential for SSVEPs detection.