文献类型：期刊文献

英文题名：Internal Feature Selection Method of CSP Based on L1-Norm and Dempster-Shafer Theory

作者：Jin, Jing[1];Xiao, Ruocheng[1];Daly, Ian[2];Miao, Yangyang[1];Wang, Xingyu[1];Cichocki, Andrzej[3,4]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China;[2]Univ Essex, Brain Comp Interfacing & Neural Engn Lab, Sch Comp Sci & Elect Engn, Colchester CO4 3SQ, Essex, England;[3]Skolkovo Inst Sci & Technol Skoltech, Moscow 121205, Russia;[4]Nicolaus Copernicus Univ UMK, PL-87100 Torun, Poland

年份：2021

卷号：32

期号：11

起止页码：4814

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

收录：;EI(收录号：20214711189558);WOS:【SCI-EXPANDED(收录号:WOS:000711638200008)】；

基金：This work was supported in part by the National Key Research and Development Program under Grant 2017YFB13003002, Grant2018YFC2002300, 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/NZG/00354.

语种：英文

外文关键词：Feature extraction; Linear programming; Electroencephalography; Optimization; Eigenvalues and eigenfunctions; Covariance matrices; Learning systems; Brain-computer interface (BCI); common spatial pattern (CSP); feature selection; motor imagery (MI); spatial filtering

摘要：The common spatial pattern (CSP) algorithm is a well-recognized spatial filtering method for feature extraction in motor imagery (MI)-based brain-computer interfaces (BCIs). However, due to the influence of nonstationary in electroencephalography (EEG) and inherent defects of the CSP objective function, the spatial filters, and their corresponding features are not necessarily optimal in the feature space used within CSP. In this work, we design a new feature selection method to address this issue by selecting features based on an improved objective function. Especially, improvements are made in suppressing outliers and discovering features with larger interclass distances. Moreover, a fusion algorithm based on the Dempster-Shafer theory is proposed, which takes into consideration the distribution of features. With two competition data sets, we first evaluate the performance of the improved objective functions in terms of classification accuracy, feature distribution, and embeddability. Then, a comparison with other feature selection methods is carried out in both accuracy and computational time. Experimental results show that the proposed methods consume less additional computational cost and result in a significant increase in the performance of MI-based BCI systems.