【摘 要】
：
Brain signals tend to be represented by a vector or a matrix to facilitate data processing and analysis with generally understood methodologies like time-se
【机 构】
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Department of Biomedical Engineering,Faculty of Electronic Information and Electrical Engineering,Da
【出 处】
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2015年中国生物医学工程联合学术年会
论文部分内容阅读
Brain 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,brain 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 brain signals with three aspects.The first is about the existing modes and tensors of brain signals and particularly,electroencephalography(EEG)and functional magnetic resonance imaging(fMRI)are taken for the example in this paper.Secondly,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 brain signals(EEG and fMRI)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.
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