Tucker Decomposition for Extracting Shared and Individual Spatial Maps from Multi-Subject Resting-State fMRI Data-Reference-Cited by-同舟云学术

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Tucker Decomposition for Extracting Shared and Individual Spatial Maps from Multi-Subject Resting-State fMRI Data

Published:2021-06-06 Issue: Volume: Page:
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Container-title:ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Author:

Han Yue,Lin Qiu-Hua,Kuang Li-Dan,Gong Xiao-Feng,Cong Fengyu,Calhoun Vince D.

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Dalian University of Technology

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9413349/9413350/09413958.pdf?arnumber=9413958

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A core tensor sparsity enhancement method for solving Tucker-2 model of multi-subject fMRI data;Biomedical Signal Processing and Control;2024-09

2. Extraction of One Time Point Dynamic Group Features via Tucker Decomposition of Multi-subject FMRI Data: Application to Schizophrenia;Communications in Computer and Information Science;2023-11-26

3. Exploring the feasibility of tensor decomposition for analysis of fNIRS signals: a comparative study with grand averaging method;Frontiers in Neuroscience;2023-08-10

4. Multidimensional Analysis of Functional Near-Infrared Spectroscopy (fNIRS) Signal using Tucker Decomposition;2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC);2022-10-09

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