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Brain State Decoding Based on fMRI Using Semisupervised Sparse Representation Classifications
المؤلفون المشاركون
Yao, Li
Zhang, Jing
Zhang, Chuncheng
Zhao, Xiaojie
Long, Zhiying
المصدر
Computational Intelligence and Neuroscience
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-12، 12ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-04-19
دولة النشر
مصر
عدد الصفحات
12
التخصصات الرئيسية
الملخص 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.
نمط استشهاد جمعية علماء النفس الأمريكية (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
نمط استشهاد الجمعية الأمريكية للغات الحديثة (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
نمط استشهاد الجمعية الطبية الأمريكية (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
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1130688
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
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