文献类型：期刊文献

英文题名：Correlation-based channel selection and regularized feature optimization for MI-based BCI

作者：Jin, Jing[1];Miao, Yangyang[1];Daly, Ian[2];Zuo, Cili[1];Hu, Dewen[3];Cichocki, Andrzej[4,5,6]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai, Peoples R China;[2]Univ Essex, Sch Comp Sci & Elect Engn, Brain Comp Interfaces & Neural Engn Lab, Wivenhoe Pk, Colchester CO4 3SQ, Essex, England;[3]Natl Univ Def Technol, Coll Mechatron Engn & Automat, Changsha 410073, Hunan, Peoples R China;[4]Skolkowo Inst Sci & Technol SKOLTECH, Moscow 143026, Russia;[5]Syst Res Inst PAS, Warsaw, Poland;[6]Nicolaus Copernicus Univ UMK, Torun, Poland

年份：2019

卷号：118

起止页码：262

外文期刊名：NEURAL NETWORKS

收录：;EI(收录号：20193007220839);WOS:【SCI-EXPANDED(收录号:WOS:000483920500023)】；

基金：This work was supported by the National key research and development program 2017YFB13003002. This work was also supported in part by the Grant National Natural Science Foundation of China, under Grant Nos. 61573142, 61773164 and 91420302, and the programme of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017.

语种：英文

外文关键词：Brain-computer interface (BCI); Electroencephalogram (EEG); Motor imagery (MI); Common spatial pattern (CSP); Channel selection; Support vector machine (SVM)

摘要：Multi-channel EEG data are usually necessary for spatial pattern identification in motor imagery (MI)-based brain computer interfaces (BCIs). To some extent, signals from some channels containing redundant information and noise may degrade BCI performance. We assume that the channels related to MI should contain common information when participants are executing the MI tasks. Based on this hypothesis, a correlation-based channel selection (CCS) method is proposed to select the channels that contained more correlated information in this study. The aim is to improve the classification performance of MI-based BCIs. Furthermore, a novel regularized common spatial pattern (RCSP) method is used to extract effective features. Finally, a support vector machine (SVM) classifier with the Radial Basis Function (RBF) kernel is trained to accurately identify the MI tasks. An experimental study is implemented on three public EEG datasets (BCI competition IV dataset1, BCI competition III dataset IVa and BCI competition III dataset Ma) to validate the effectiveness of the proposed methods. The results show that the CCS algorithm obtained superior classification accuracy (78% versus 56.4% for dataset1, 86.6% versus 76.5% for dataset 2 and 91.3% versus 85.1% for dataset 3) compared to the algorithm using all channels (AC), when CSP is used to extract the features. Furthermore, RCSP could further improve the classification accuracy (81.6% for datasetl, 87.4% for dataset2 and 91.9% for dataset 3), when CCS is used to select the channels. (C) 2019 Elsevier Ltd. All rights reserved.