文献类型：期刊文献

英文题名：Variance characteristic preserving common spatial pattern for motor imagery BCI

作者：Liang, Wei[1];Jin, Jing[1,2];Xu, Ren[3];Wang, Xingyu[1];Cichocki, Andrzej[4,5]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai, Peoples R China;[2]East China Univ Sci & Technol, Shenzhen Res Inst, Shenzhen, Peoples R China;[3]g Tec Med Engn GmbH, Schiedlberg, Austria;[4]Polish Acad Sci, Syst Res Inst, Warsaw, Poland;[5]Nicolaus Copernicus Univ, Dept Informat, Torun, Poland

年份：2023

卷号：17

外文期刊名：FRONTIERS IN HUMAN NEUROSCIENCE

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001105206900001)】；

基金：This work was supported by STI 2030-major projects 2022ZD0208900 and the Grant National Natural Science Foundation of China under Grant 62176090; in part by 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; This research is also supported by National Government GuidedSpecial Funds for Local Science and Technology Development (Shenzhen, China) (No. 2021Szvup043) and by Project of Jiangsu Province Science and Technology Plan Special Fund in 2022 (Key research and development plan industry foresight and key core technologies) under Grant BE2022064-1.

语种：英文

外文关键词：motor imagery; EEG; brain-computer interface; common spatial pattern; variance characteristic preserving

摘要：IntroductionThe common spatial patterns (CSP) algorithm is the most popular technique for extracting electroencephalogram (EEG) features in motor imagery based brain-computer interface (BCI) systems. CSP algorithm embeds the dimensionality of multichannel EEG data to extract features of motor imagery tasks. Most previous studies focused on the optimization of the time domain and the spectrum domain of EEG signal to improve the effectiveness of CSP, whereas ignoring the constraint on the projected feature space.MethodsThis study proposed a variance characteristic preserving CSP (VPCSP) that is modified by a regularization item based on graph theory. Specifically, we calculated the loss of abnormalities of the projected data while preserving the variance characteristic locally. Then the loss could be rewritten as a matrix with the introduction of the Laplace matrix, which turned it into a generalized eigenvalue problem equivalent to CSP. This study evaluated the proposed method on two public EEG datasets from the BCI competition. The modified method could extract robust and distinguishable features that provided higher classification performance. Experimental results showed that the proposed regularization improved the effectiveness of CSP significantly and achieved superior performance compared with reported modified CSP algorithms significantly.ResultsThe classification accuracy of the proposed method achieved 87.88 %, 90.07 %, and 76.06 % on public dataset IV part I, III part IVa and the self-collected dataset, respectively. Comparative experiments are conducted on two public datasets and one self-collected dataset. Results showed that the proposed method outperformed the reported algorithm.DiscussionThe proposed method can extract robust features to increase the performance of BCI systems. And the proposal still has expandability. These results show that our proposal is a promising candidate for the performance improvement of MI-BCI.