Bayesian tensor factorization for multi-way analysis of multi-dimensional EEG

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成果类型：

期刊论文

作者：

Tang, Yunbo;Chen, Dan*;Wang, Lizhe;Zomaya, Albert Y.;Chen, Jingying;...

通讯作者：

Chen, Dan

作者机构：

[Chen, Dan; Tang, Yunbo] Wuhan Univ, Sch Comp Sci, Wuhan 430072, Hubei, Peoples R China.

[Wang, Lizhe] China Univ Geosci, Sch Comp Sci, Wuhan 430074, Hubei, Peoples R China.

[Zomaya, Albert Y.] Univ Sydney, Sch Informat Technol, Sydney, NSW, Australia.

[Chen, Jingying] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.

[Liu, Honghai] Shanghai Jiao Tong Univ, Sch Mech Engn, State Key Lab Mech Syst & Vibrat, Shanghai 200240, Peoples R China.

通讯机构：

[Chen, Dan] W

Wuhan Univ, Sch Comp Sci, Wuhan 430072, Hubei, Peoples R China.

语种：

英文

关键词：

Bayesian tensor factorization;Big time series data;Denoising;Multi-dimensional EEG;Multi-way analysis;Rank reduction

期刊：

Neurocomputing

ISSN：

0925-2312

年：

2018

卷：

318

页码：

162-174

DOI：

10.1016/j.neucom.2018.08.045

基金类别：

National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61772380]; Foundation for Innovative Research Groups of Hubei Province [2017CFA007]

机构署名：

本校为其他机构

院系归属：

国家数字化学习工程技术研究中心

摘要：

Factorization-based analysis of multi-dimensional EEG (Electroencephalography) has become increasingly important in neuroscience research and practices with the capability of extracting latent multi-way features. However, how to sift the most informative factors of routinely noisy EEG remains unclear especially under the circumstance of no a priori knowledge. This study proposes a Bayesian tensor factorization (BTF) model as a "one-stop" solution to the challenges. BTF assumes non-informative priori on potential distribution of factors and noise derived from exponential family distribution. A ...

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Bayesian tensor factorization for multi-way analysis of multi-dimensional EEG

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