文献类型：期刊文献

英文题名：Bispectrum-Based Channel Selection for Motor Imagery Based Brain-Computer Interfacing

作者：Jin, Jing[1];Liu, Chang[1];Daly, Ian[2];Miao, Yangyang[1];Li, Shurui[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, Sch Comp Sci & Elect Engn, Brain Comp Interfacing & Neural Engn Lab, Colchester CO4 3SQ, Essex, England;[3]Skolkovo Inst Sci & Technol Skoltech, Moscow 121205, Russia;[4]Nicolaus Copernicus Univ UMK, Dept Appl Comp Sci, PL-87100 Torun, Poland

年份：2020

卷号：28

期号：10

起止页码：2153

外文期刊名：IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING

收录：;EI(收录号：20204209356696);WOS:【SCI-EXPANDED(收录号:WOS:000578017200006)】；

基金：This work was supported in part by the National Key Research and Development Program under Grant 2017YFB13003002; in part by the Grant National Natural Science Foundation of China, under Grant 61573142, Grant 61773164, and Grant 91420302; in part by the Programme of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017; in part by the Ministry of Education and Science of the Russian Federation under Grant 14.756.31.0001; in part by the Polish National Science Center under GrantUMO-2016/20/W/NZG/00354; and in part by the ShuGuang Project supported by the Shanghai Municipal Education Commission and the Shanghai Education Development Foundation under Grant 19SG25.

语种：英文

外文关键词：Brain-computer interface; motor imagery; electroencephalogram (EEG); bispectrum analysis; channel selection

摘要：The performance of motor imagery (MI) based Brain-computer interfacing (BCI) is easily affected by noise and redundant information that exists in the multi-channel electroencephalogram (EEG). To solve this problem, many temporal and spatial feature based channel selection methods have been proposed. However, temporal and spatial features do not accurately reflect changes in the power of the oscillatory EEG. Thus, spectral features of MI-related EEG signals may be useful for channel selection. Bispectrum analysis is a technique developed for extracting non-linear and non-Gaussian information from non-linear and non-Gaussian signals. The features extracted from bispectrum analysis can provide frequency domain information about the EEG. Therefore, in this study, we propose a bispectrum-based channel selection (BCS) method for MI-based BCI. The proposed method uses the sum of logarithmic amplitudes (SLA) and the first order spectral moment (FOSM) features extracted from bispectrum analysis to select EEG channels without redundant information. Three public BCI competition datasets (BCI competition IV dataset 1, BCI competition III dataset IVa and BCI competition III dataset IIIa) were used to validate the effectiveness of our proposed method. The results indicate that our BCS method outperforms use of all channels (83.8% vs 69.4%, 86.3% vs 82.9% and 77.8% vs 68.2%, respectively). Furthermore, compared to the other state-of-the-art methods, our BCS method also can achieve significantly better classification accuracies for MI-based BCI (Wilcoxon signed test, p < 0.05).