文献类型：期刊文献

英文题名：Towards correlation-based time window selection method for motor imagery BCIs

作者：Feng, Jiankui[1];Yin, Erwei[2];Jin, Jing[1];Saab, Rami[1];Daly, Ian[3];Wang, Xingyu[1];Hu, Dewen[4];Cichocki, Andrzej[5,6,7]

机构：[1]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai, Peoples R China;[2]Acad Mil Sci China, Natl Inst Def Technol Innovat, Beijing 100081, Peoples R China;[3]Univ Essex, Brain Comp Interfaces & Neural Engn Lab, Sch Comp Sci & Elect Engn, Wivenhoe Pk, Colchester CO4 3SQ, Essex, England;[4]Natl Univ Def Technol, Coll Mechatron Engn & Automat, Changsha 410073, Hunan, Peoples R China;[5]RIKEN, Brain Sci Inst, Lab Adv Brain Signal Proc, Wako, Saitama, Japan;[6]Syst Res Inst PAS, Warsaw, Poland;[7]Nicolaus Copernicus Univ UMK, Torun, Poland

年份：2018

卷号：102

起止页码：87

外文期刊名：NEURAL NETWORKS

收录：;EI(收录号：20181204926544);WOS:【SCI-EXPANDED(收录号:WOS:000429306200009)】；

基金：This work was supported in part by the Grant National Natural Science Foundation of China, under Grant Nos. 91420302, 61573142 and 61703407. This work was also supported by the National key research and development program 2017YFB13003002, the programme of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017, and the Foundation of Key Laboratory of Science and Technology for National Defense (No. 6142222030301).

语种：英文

外文关键词：Brain-computer interface; Correlation; Feature extraction; Time window selection; Common spatial pattern

摘要：The start of the cue is often used to initiate the feature window used to control motor imagery (MI)-based brain-computer interface (BCI) systems. However, the time latency during an MI period varies between trials for each participant. Fixing the starting time point of MI features can lead to decreased system performance in MI-based BCI systems. To address this issue, we propose a novel correlation-based time window selection (CTWS) algorithm for MI-based BCIs. Specifically, the optimized reference signals for each class were selected based on correlation analysis and performance evaluation. Furthermore, the starting points of time windows for both training and testing samples were adjusted using correlation analysis. Finally, the feature extraction and classification algorithms were used to calculate the classification accuracy. With two datasets, the results demonstrate that the CTWS algorithm significantly improved the system performance when compared to directly using feature extraction approaches. Importantly, the average improvement in accuracy of the CTWS algorithm on the datasets of healthy participants and stroke patients was 16.72% and 5.24%, respectively when compared to traditional common spatial pattern (CSP) algorithm. In addition, the average accuracy increased 7.36% and 9.29%, respectively when the CTWS was used in conjunction with Sub-Alpha-Beta Log-Det Divergences (Sub-ABLD) algorithm. These findings suggest that the proposed CTWS algorithm holds promise as a general feature extraction approach for MI-based BCIs. (c) 2018 Elsevier Ltd. All rights reserved.