文献类型：期刊文献

英文题名：An Optimized Channel Selection Method Based on Multifrequency CSP-Rank for Motor Imagery-Based BCI System

作者：Feng, Jian Kui[1];Jin, Jing[1];Daly, Ian[2];Zhou, Jiale[1];Niu, Yugang[1];Wang, Xingyu[1];Cichocki, Andrzej[3,4,5]

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 Interfacing & Neural Engn Lab, Colchester CO4 3SQ, Essex, England;[3]Skolkowo Inst Sci & Technol SKOLTECH, Moscow 143026, Russia;[4]Syst Res Inst PAS, Warsaw, Poland;[5]Nicolaus Copernicus Univ UMK, Torun, Poland

年份：2019

卷号：2019

外文期刊名：COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE

收录：;EI(收录号：20192306998233);WOS:【SCI-EXPANDED(收录号:WOS:000469190300001)】；

基金：This work was supported by the National Key Research and Development Program (2017YFB13003002). This work was also supported in part by the 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 no. B17017.

语种：英文

外文关键词：Biomedical signal processing - Electroencephalography - Discriminant analysis

摘要：Background. Due to the redundant information contained in multichannel electroencephalogram (EEG) signals, the classification accuracy of brain-computer interface (BCI) systems may deteriorate to a large extent. Channel selection methods can help to remove task-independent electroencephalogram (EEG) signals and hence improve the performance of BCI systems. However, in different frequency bands, brain areas associated with motor imagery are not exactly the same, which will result in the inability of traditional channel selection methods to extract effective EEG features. New Method. To address the above problem, this paper proposes a novel method based on common spatial pattern- (CSP-) rank channel selection for multifrequency band EEG (CSP-R-MF). It combines the multiband signal decomposition filtering and the CSP-rank channel selection methods to select significant channels, and then linear discriminant analysis (LDA) was used to calculate the classification accuracy. Results. The results showed that our proposed CSP-R-MF method could significantly improve the average classification accuracy compared with the CSP-rank channel selection method.