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Brain State Decoding Based on fMRI Using Semisupervised Sparse Representation Classifications
Joint Authors
Yao, Li
Zhang, Jing
Zhang, Chuncheng
Zhao, Xiaojie
Long, Zhiying
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-04-19
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Zhang, Jing& Zhang, Chuncheng& Yao, Li& Zhao, Xiaojie& Long, Zhiying. 2018. Brain State Decoding Based on fMRI Using Semisupervised Sparse Representation Classifications. Computational Intelligence and Neuroscience،Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1130688
Modern Language Association (MLA)
Zhang, Jing…[et al.]. Brain State Decoding Based on fMRI Using Semisupervised Sparse Representation Classifications. Computational Intelligence and Neuroscience No. 2018 (2018), pp.1-12.
https://search.emarefa.net/detail/BIM-1130688
American Medical Association (AMA)
Zhang, Jing& Zhang, Chuncheng& Yao, Li& Zhao, Xiaojie& Long, Zhiying. Brain State Decoding Based on fMRI Using Semisupervised Sparse Representation Classifications. Computational Intelligence and Neuroscience. 2018. Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1130688
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1130688