龚晓峰

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教授

博士生导师

硕士生导师

主要任职:信息与通信工程学院副院长

其他任职:电子技术教研室主任

性别:男

毕业院校:北京理工大学

学位:博士

所在单位:信息与通信工程学院

学科:通信与信息系统. 信号与信息处理

办公地点:海山楼B511

联系方式:QQ:51574683

电子邮箱:xfgong@dlut.edu.cn

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Tensor decomposition of EEG signals: A brief review

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论文类型:期刊论文

第一作者:Cong, Fengyu

通讯作者:Cong, FY (reprint author), Dalian Univ Technol, Dept Biomed Engn, Dalian 116024, Peoples R China.

合写作者:Lin, Qiu-Hua,Kuang, Li-Dan,Gong, Xiao-Feng,Astikainen, Piia,Ristaniemi, Tapani

发表时间:2015-06-15

发表刊物:JOURNAL OF NEUROSCIENCE METHODS

收录刊物:SCIE、PubMed、Scopus

卷号:248

页面范围:59-69

ISSN号:0165-0270

关键字:Event-related potentials; EEG; Tensor decomposition; Canonical polyadic; Tucker; Brain; Signal

摘要:Electroencephalography (EEG) is one fundamental tool for functional brain imaging. EEG signals tend to be represented by a vector or a matrix to facilitate data processing and analysis with generally understood methodologies like time-series analysis, spectral analysis and matrix decomposition. Indeed, EEG signals are often naturally born with more than two modes of time and space, and they can be denoted by a multi-way array called as tensor. This review summarizes the current progress of tensor decomposition of EEG signals with three aspects. The first is about the existing modes and tensors of EEG signals. Second, two fundamental tensor decomposition models, canonical polyadic decomposition (CPD, it is also called parallel factor analysis-PARAFAC) and Tucker decomposition, are introduced and compared. Moreover, the applications of the two models for EEG signals are addressed. Particularly, the determination of the number of components for each mode is discussed. Finally, the N-way partial least square and higher-order partial least square are described for a potential trend to process and analyze brain signals of two modalities simultaneously. (C) 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license.