2 March 2018 Brain decoding using deep convolutional network and its application in cross-subject analysis
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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.
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Yufei Gao, Yufei Gao, Yameng Zhang, Yameng Zhang, Wen Zhou, Wen Zhou, Li Yao, Li Yao, Jiacai Zhang, Jiacai Zhang, } "Brain decoding using deep convolutional network and its application in cross-subject analysis ", Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 1057423 (2 March 2018); doi: 10.1117/12.2286764; https://doi.org/10.1117/12.2286764
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