Sparse Representation of Complex-Valued fMRI Data Based on Hard Thresholding of Spatial Source Phase

2021 ◽  
Author(s):  
Jia-Yang Song ◽  
Miao-Ying Qi ◽  
Dun-Pei Lv ◽  
Chao-Ying Zhang ◽  
Qiu-Hua Lin ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Fmri Data ◽  
Hard Thresholding ◽  
Source Phase ◽  
Complex Valued ◽  
Spatial Source

scholarly journals Spatial source phase: A new feature for identifying spatial differences based on complex‐valued resting‐state fMRI data

2019 ◽  
Vol 40 (9) ◽  
pp. 2662-2676 ◽  
Author(s):  
Yue Qiu ◽  
Qiu‐Hua Lin ◽  
Li‐Dan Kuang ◽  
Xiao‐Feng Gong ◽  
Fengyu Cong ◽  
...  
Keyword(s):  
Resting State ◽  
Resting State Fmri ◽  
Fmri Data ◽  
Spatial Differences ◽  
Source Phase ◽  
New Feature ◽  
Complex Valued ◽  
Spatial Source

Sparse representation of complex-valued fMRI data based on spatiotemporal concatenation of real and imaginary parts

2020 ◽  
pp. 109047
Author(s):  
Chao-Ying Zhang ◽  
Qiu-Hua Lin ◽  
Li-Dan Kuang ◽  
Wei-Xing Li ◽  
Xiao-Feng Gong ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Fmri Data ◽  
Complex Valued

Embedded sparse representation of fMRI data via group-wise dictionary optimization

2016 ◽  
Author(s):  
Dajiang Zhu ◽  
Binbin Lin ◽  
Joshua Faskowitz ◽  
Jieping Ye ◽  
Paul M. Thompson
Keyword(s):  
Sparse Representation ◽  
Fmri Data

scholarly journals Shift-Invariant Canonical Polyadic Decomposition of Complex-Valued Multi-Subject fMRI Data With a Phase Sparsity Constraint

2020 ◽  
Vol 39 (4) ◽  
pp. 844-853
Author(s):  
Li-Dan Kuang ◽  
Qiu-Hua Lin ◽  
Xiao-Feng Gong ◽  
Fengyu Cong ◽  
Yu-Ping Wang ◽  
...  
Keyword(s):  
Fmri Data ◽  
Sparsity Constraint ◽  
Canonical Polyadic Decomposition ◽  
Complex Valued

scholarly journals Brain State Decoding Based on fMRI Using Semisupervised Sparse Representation Classifications

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Jing Zhang ◽  
Chuncheng Zhang ◽  
Li Yao ◽  
Xiaojie Zhao ◽  
Zhiying Long
Keyword(s):  
Sparse Representation ◽  
Supervised Learning ◽  
Reconstruction Error ◽  
Classification Performance ◽  
Semisupervised Learning ◽  
Fmri Data ◽  
Training Dataset ◽  
Brain State ◽  
Classification Techniques ◽  
Average Coefficient

Multivariate classification techniques have been widely applied to decode brain states using functional magnetic resonance imaging (fMRI). Due to variabilities in fMRI data and the limitation of the collection of human fMRI data, it is not easy to train an efficient and robust supervised-learning classifier for fMRI data. Among various classification techniques, sparse representation classifier (SRC) exhibits a state-of-the-art classification performance in image classification. However, SRC has rarely been applied to fMRI-based decoding. This study aimed to improve SRC using unlabeled testing samples to allow it to be effectively applied to fMRI-based decoding. We proposed a semisupervised-learning SRC with an average coefficient (semiSRC-AVE) method that performed the classification using the average coefficient of each class instead of the reconstruction error and selectively updated the training dataset using new testing data with high confidence to improve the performance of SRC. Simulated and real fMRI experiments were performed to investigate the feasibility and robustness of semiSRC-AVE. The results of the simulated and real fMRI experiments showed that semiSRC-AVE significantly outperformed supervised learning SRC with an average coefficient (SRC-AVE) method and showed better performance than the other three semisupervised learning methods.

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