文献类型：期刊文献

英文题名：Multi-kernel extreme learning machine for EEG classification in brain-computer interfaces

作者：Zhang, Yu[1];Wang, Yu[2];Zhou, Guoxu[3];Jin, Jing[1];Wang, Bei[1];Wang, Xingyu[1];Cichocki, Andrzej[4,5]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai, Peoples R China;[2]Shanghai Ruanzhong Informat Technol Co Ltd, Shanghai, Peoples R China;[3]Guangdong Univ Technol, Sch Automat, Guangzhou, Guangdong, Peoples R China;[4]RIKEN, Brain Sci Inst, Lab Adv Brain Signal Proc, Wako, Saitama, Japan;[5]Skolkovo Inst Sci & Technol SKOLTECH, Moscow 143026, Russia

年份：2018

卷号：96

起止页码：302

外文期刊名：EXPERT SYSTEMS WITH APPLICATIONS

收录：;EI(收录号：20175104551906);WOS:【SCI-EXPANDED(收录号:WOS:000424176900023)】；

基金：This work was supported in part by the grant National Natural Science Foundation of China, under Grant nos. 91420302, 61573142, 61673124. This work was also supported by the Fundamental Research Funds for the Central Universities WH1516018, Shanghai Chenguang Program under Grant 14CG3 and Shanghai Natural Science Foundation under Grant 16ZR1407500, the Programme of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017, the MES RF grant 14.756.310001, and the PNSC grant 2016/20/W/NZ/00354.

语种：英文

外文关键词：Brain-computer interface; Electroencephalogram; Extreme learning machine; Multi-kernel learning; Motor imagery

摘要：One of the most important issues for the development of a motor-imagery based brain-computer interface (BCI) is how to design a powerful classifier with strong generalization capability. Extreme learning machine (ELM) has recently proven to be comparable or more efficient than support vector machine for many pattern recognition problems. In this study, we propose a multi-kernel ELM (MKELM)-based method for motor imagery electroencephalogram (EEG) classification. The kernel extension of ELM provides an elegant way to circumvent calculation of the hidden layer outputs and inherently encode it in a kernel matrix. We investigate effects of two different kernel functions (i.e., Gaussian kernel and polynomial kernel) on the performance of kernel ELM. The MKELM method is subsequently developed by integrating these two types of kernels with a multi-kernel learning strategy, which can effectively explore the supplementary information from multiple nonlinear feature spaces for more robust classification of EEG. An extensive experimental comparison with two public EEG datasets indicates that the MKELM method gives higher classification accuracy than those of the other competing algorithms. The experimental results confirm that superiority of the proposed MKELM-based method for accurate classification of EEG associated with motor imagery in BCI applications. Our method also provides a promising and generalized solution to investigate the complex and nonlinear information for various applications in the fields of expert and intelligent systems. (C) 2017 Elsevier Ltd. All rights reserved.