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Proceedings Paper

Brain decoding using deep convolutional network and its application in cross-subject analysis
Author(s): Yufei Gao; Yameng Zhang; Wen Zhou; Li Yao; Jiacai Zhang
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Paper Abstract

Recent advances in functional magnetic resonance imaging (fMRI) techniques and machine learning have shown that it is possible to decode distinct brain state from complex brain activities, which have raised widespread concern. Deep learning is a popular method of machine learning and has achieved remarkable results in the field of speech recognition, image recognition and so on. However, there are many challenges in medical image analysis when using deep learning. Aiming to solve the difficulty of subject-transfer decoding, high dimensional feature extraction and slow computation, here we proposed a deep convolutional decoding (DCD) model. First, an architecture of deep convolutional network became a subject-transfer feature extractor on task-fMRI (tfMRI) data. Then, the high dimensional abstract feature was used to identify certain brain cognitive state. The experimental results show that our proposed method can achieve higher decoding accuracy of brain state across different subjects compared with traditional methods.

Paper Details

Date Published: 2 March 2018
PDF: 9 pages
Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 1057423 (2 March 2018); doi: 10.1117/12.2286764
Show Author Affiliations
Yufei Gao, Beijing Normal Univ. (China)
Yameng Zhang, Beijing Normal Univ. (China)
Wen Zhou, Univ. of Rochester (United States)
Li Yao, Beijing Normal Univ. (China)
Jiacai Zhang, Beijing Normal Univ. (China)

Published in SPIE Proceedings Vol. 10574:
Medical Imaging 2018: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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