Knowledge Commons of Institute of Automation,CAS
Reconstructing Perceived Images From Human Brain Activities With Bayesian Deep Multiview Learning | |
Du, Changde1,2; Du, Changying3,4; Huang, Lijie1,2; He, Huiguang1,2,5 | |
发表期刊 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS |
ISSN | 2162-237X |
2019-08-01 | |
卷号 | 30期号:8页码:2310-2323 |
摘要 | Neural decoding, which aims to predict external visual stimuli information from evoked brain activities, plays an important role in understanding human visual system. Many existing methods are based on linear models, and most of them only focus on either the brain activity pattern classification or visual stimuli identification. Accurate reconstruction of the perceived images from the measured human brain activities still remains challenging. In this paper, we propose a novel deep generative multiview model for the accurate visual image reconstruction from the human brain activities measured by functional magnetic resonance imaging (fMRI). Specifically, we model the statistical relationships between the two views (i.e., the visual stimuli and the evoked fMRI) by using two view-specific generators with a shared latent space. On the one hand, we adopt a deep neural network architecture for visual image generation, which mimics the stages of human visual processing. On the other hand, we design a sparse Bayesian linear model for fMRI activity generation, which can effectively capture voxel correlations, suppress data noise, and avoid overfitting. Furthermore, we devise an efficient mean-field variational inference method to train the proposed model. The proposed method can accurately reconstruct visual images via Bayesian inference. In particular, we exploit a posterior regularization technique in the Bayesian inference to regularize the model posterior. The quantitative and qualitative evaluations conducted on multiple fMRI data sets demonstrate the proposed method can reconstruct visual images more accurately than the state of the art. |
关键词 | Deep neural network (DNN) image reconstruction multiview learning neural decoding variational Bayesian inference |
DOI | 10.1109/TNNLS.2018.2882456 |
关键词[WOS] | NEURAL-NETWORKS ; NATURAL IMAGES ; FMRI ; REPRESENTATIONS ; CATEGORIES ; PATTERNS ; OBJECTS ; MODELS ; FACES |
收录类别 | SCI |
语种 | 英语 |
资助项目 | Beijing Municipal Science and Technology Commission[Z181100008918010] ; CAS Scientific Equipment Development Project[YJKYYQ20170050] ; National Natural Science Foundation of China[61602449] ; Strategic Priority Research Program of CAS ; National Natural Science Foundation of China[91520202] ; Youth Innovation Promotion Association CAS ; Youth Innovation Promotion Association CAS ; National Natural Science Foundation of China[91520202] ; Strategic Priority Research Program of CAS ; National Natural Science Foundation of China[61602449] ; CAS Scientific Equipment Development Project[YJKYYQ20170050] ; Beijing Municipal Science and Technology Commission[Z181100008918010] |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000476787300006 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
是否为代表性论文 | 是 |
七大方向——子方向分类 | 模式识别基础 |
国重实验室规划方向分类 | 认知机理与类脑学习 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/27782 |
专题 | 脑图谱与类脑智能实验室_神经计算与脑机交互 |
通讯作者 | He, Huiguang |
作者单位 | 1.Chinese Acad Sci, Inst Automat, Res Ctr Brain Inspired Intelligence, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Inst Software, Lab Parallel Software & Computat Sci, Beijing 100190, Peoples R China 4.360 Search Lab, Beijing 100015, Peoples R China 5.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Beijing 100190, Peoples R China |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Du, Changde,Du, Changying,Huang, Lijie,et al. Reconstructing Perceived Images From Human Brain Activities With Bayesian Deep Multiview Learning[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2019,30(8):2310-2323. |
APA | Du, Changde,Du, Changying,Huang, Lijie,&He, Huiguang.(2019).Reconstructing Perceived Images From Human Brain Activities With Bayesian Deep Multiview Learning.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,30(8),2310-2323. |
MLA | Du, Changde,et al."Reconstructing Perceived Images From Human Brain Activities With Bayesian Deep Multiview Learning".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 30.8(2019):2310-2323. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
TNNLS_2019_Reconstru(3773KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 |
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