文献类型：期刊文献

英文题名：Motor Imagery EEG Classification Based on Riemannian Sparse Optimization and Dempster-Shafer Fusion of Multi-Time-Frequency Patterns

作者：Jin, Jing[1,2];Qu, Tingnan[1];Xu, Ren[3];Wang, Xingyu[1];Cichocki, Andrzej[4,5,6]

机构：[1]Minist Educ, Key Lab Smart Mfg Energy Chem Proc, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Shenzhen Res Inst, Shenzhen 518063, Peoples R China;[3]Gtec Med Engn GmbH, A-4521 Schiedlberg, Austria;[4]Skolkovo Inst Sci & Technol SKOLTECH, Moscow 143026, Russia;[5]Polish Acad Sci, Syst Res Inst, PL-01447 Warsaw, Poland;[6]Nicolaus Copernicus Univ, Dept Informat, PL-87100 Torun, Poland

年份：2023

卷号：31

起止页码：58

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

收录：;EI(收录号：20224513093244);WOS:【SCI-EXPANDED(收录号:WOS:000966218900001)】；

基金：This work was supported in part by the Science and Technology Innovation 2030 Major Projects under Grant 2022ZD0208900; in part by the Grant National Natural Science Foundation of China under Grant 62176090; in part by the Shanghai Municipal Science and Technology Major Project under Grant 2021SHZDZX; 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; in part by the Polish National Science Center under Grant UMO-2016/20/W/NZ4/00354; and in part by the National Government Guided Special Funds for Local Science and Technology Development, Shenzhen, China, under Grant 2021Szvup043.

语种：英文

外文关键词：Feature extraction; Covariance matrices; Electroencephalography; Support vector machines; Time-frequency analysis; Optimization; Task analysis; Brain-computer interface; motor imagery; Riemannian geometry; sparse optimization; Dempster-Shafer theory

摘要：Motor imagery-based brain-computer interfaces (MI-BCIs) features are generally extracted from a wide fixed frequency band and time window of EEG signal. The performance suffers from individual differences in corresponding time to MI tasks. In order to solve the problem, in this study, we propose a novel method named Riemannian sparse optimization and Dempster-Shafer fusion of multi-time-frequency patterns (RSODSF) to enhance the decoding efficiency. First, we effectively combine the Riemannian geometry of the spatial covariance matrix with sparse optimization to extract more robust and distinct features. Second, the Dempster-Shafer theory is introduced and used to fuse each time window after sparse optimization of Riemannian features. Besides, the probabilistic values of the support vector machine (SVM) are obtained and transformed to effectively fuse multiple classifiers to leverage potential soft information of multiple trained SVM. The open-access BCI Competition IV dataset IIa and Competition III dataset IIIa are employed to evaluate the performance of the proposed RSODSF. It achieves higher average accuracy (89.7% and 96.8%) than state-of-the-art methods. The improvement over the common spatial patterns (SFBCSP) are respectively 9.9% and 12.4% (p < 0.01, paired t-test). These results show that our proposed RSODSF method is a promising candidate for the performance improvement of MI-BCI.