[Eeglablist] Tensor Decomposition of EEG Signals

Makoto Miyakoshi mmiyakoshi at ucsd.edu
Wed Jun 3 11:28:37 PDT 2015

Dear Fengyu (rich and rich),

Congrats for publication! Without much reading, some thoughts and comments.

> Electroencephalography (EEG) is one fundamental tool for functional brain

I'm sure Scott will like this phrase.

> However, the mostly applied computing tools for brain research are
oriented for one-way or two-way data.

We do not apply ICA on concatenated multiple-subject data (which is called
group ICA) because we appreciate individual differences across subjects.
This sounds good, but it's also a curse because the group-level analysis is
so complicated. How do you integrate thousands of ICs from dozens of
subjects in the final group-level analysis? We have been struggling against
the issue over ten years.

By the way please tell me if it is possible to apply your three-way data
analysis on individual subject's independent components. I don't have
math/physic background.


On Fri, May 29, 2015 at 3:55 PM, CONG Fengyu 丛丰裕 <fengyu.cong at aliyun.com>

> Dear EEGLablist members,
> I would like to introduce my new review paper to you. It is about Tensor
> Decomposition of EEG Signals and published in Journal of Neuroscience
> Methods. The paper is open access and can be downloaded via
> http://www.sciencedirect.com/science/article/pii/S0165027015001016. The
> demo of MATLAB codes and ERP data can be downloaded via
> http://www.escience.cn/people/cong/AdvancedSP_ERP.html
> 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.
> Looking forward to your comments and suggestions on tensor decomposition
> of EEG signals.
> Sincerely,
> Fengyu
> P.S.: The bibliography of the paper is as the following:
> Fengyu Cong, Qiu-Hua Lin, Li-Dan Kuang, Xiao-Feng Gong, Piia Astikainen,
> Tapani Ristaniemi, Tensor Decomposition of EEG Signals: A Brief Review,
> Journal of Neuroscience Methods 248: 59–69, 2015.
> --
> Fengyu Cong, Ph.D., IEEE Senior Member
> Professor, Department of Biomedical Engineering, Dalian University of
> Technology, China
> Docent (Adjunct Associate Professor), University of Jyvaskyla, Finland
> Email: cong at dlut.edu.cn, fengyu.cong at aliyun.com
> Homepage: http://www.escience.cn/people/cong/index.html
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Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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