Huiguang He

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Huiguang He (34)
Changde Du (12)
Shuang Qiu (9)
Wei Wei (7)
Chunheng Wang (3)
Dong Ming (3)
Kaicheng Fu (3)
Kangning Wang (3)
Minpeng Xu (3)
Qiongyi Zhou (3)
Yaodong Li (3)
Zhongyu Huang (3)
Baihua Xiao (2)
Chaozhuo Li (2)
Dai Dai (2)
Dan Li (2)
Feiyue Wang (2)
Jing Hua (2)
Joshua T Vogelstein (2)
Zengguang Hou (2)

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Pattern Recognition (3)
IEEE Transactions on Multimedia (2)
IEEE Transactions on Pattern Analysis and Machine Intelligence (2)
Neural Networks (2)
Computer Vision and Image Understanding (1)
Computer-Aided Design (1)
Expert Systems with Applications: An International Journal (1)
IEEE Intelligent Systems (1)
IEEE Transactions on Affective Computing (1)
Information Fusion (1)
Machine Vision and Applications (1)

Proceedings/Book Names

MM '22: Proceedings of the 30th ACM International Conference on Multimedia (2)
MM '23: Proceedings of the 31st ACM International Conference on Multimedia (2)
Neural Information Processing (2)
FSKD'05: Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I (1)
ICPR '06: Proceedings of the 18th International Conference on Pattern Recognition - Volume 02 (1)
ISI'06: Proceedings of the 4th IEEE international conference on Intelligence and Security Informatics (1)
Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 (1)
Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 (1)
Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 (1)
MLMI'11: Proceedings of the Second international conference on Machine learning in medical imaging (1)
MM '18: Proceedings of the 26th ACM international conference on Multimedia (1)
PAISI'07: Proceedings of the 2007 Pacific Asia conference on Intelligence and security informatics (1)
Pattern Recognition and Computer Vision (1)
Pattern Recognition and Computer Vision (1)
WISI'06: Proceedings of the 2006 international conference on Intelligence and Security Informatics (1)

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Video (1)

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Springer-Verlag (13)
Association for Computing Machinery (5)
Elsevier Science Inc. (4)
IEEE Computer Society (3)
Elsevier Science Ltd. (2)
IEEE Press (2)
Butterworth-Heinemann (1)
Elsevier Science Publishers B. V. (1)
IEEE Computer Society Press (1)
IEEE Educational Activities Department (1)
Pergamon Press, Inc. (1)

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research-article
Contrastive fine-grained domain adaptation network for EEG-based vigilance estimation
- Kangning Wang
  Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China
  Laboratory of Brain Atlas and Brain-Inspired Intelligence, Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
  ,
- Wei Wei
  Laboratory of Brain Atlas and Brain-Inspired Intelligence, Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
  ,
- Weibo Yi
  Beijing Machine and Equipment Institute, Beijing 100854, China
  ,
- Shuang Qiu
  Laboratory of Brain Atlas and Brain-Inspired Intelligence, Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
  School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
  ,
- Huiguang He
  Laboratory of Brain Atlas and Brain-Inspired Intelligence, Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
  School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
  ,
- Minpeng Xu
  Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China
  College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China
  ,
- Dong Ming
  Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China
  College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China
Neural Networks, Volume 179, Issue C•Nov 2024 • https://doi.org/10.1016/j.neunet.2024.106617
Abstract
Vigilance state is crucial for the effective performance of users in brain-computer interface (BCI) systems. Most vigilance estimation methods rely on a large amount of labeled data to train a satisfactory model for the specific subject, which ...
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Identifying the hierarchical emotional areas in the human brain through information fusion
- Zhongyu Huang
  Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
  School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
  ,
- Changde Du
  Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
  School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
  ,
- Chaozhuo Li
  Key Laboratory of Trustworthy Distributed Computing and Service (MoE), Beijing University of Posts and Telecommunications, Beijing, 100876, China
  ,
- Kaicheng Fu
  Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
  School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
  ,
- Huiguang He
  Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
  School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
Information Fusion, Volume 113, Issue C•Jan 2025 • https://doi.org/10.1016/j.inffus.2024.102613
Abstract
The brain basis of emotion has consistently received widespread attention, attracting a large number of studies to explore this cutting-edge topic. However, the methods employed in these studies typically only model the pairwise relationship ...
Highlights

Identify hierarchical emotional areas to study brain mechanisms underlying emotion.
Conduct an in-depth theoretical analysis based on information fusion and graph theory.
Develop a novel framework to improve emotion decoding using ...
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PGCN: Pyramidal Graph Convolutional Network for EEG Emotion Recognition
- Ming Jin
  School of Automation Science and Engineering, South China University of Technology, Guangzhou, China
  ,
- Changde Du
  Research Center for Brain-Inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China
  ,
- Huiguang He
  National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
  ,
- Ting Cai
  No. 2 Hospital, Ningbo, China
  ,
- Jinpeng Li
  School of Automation Science and Engineering, South China University of Technology, Guangzhou, China
IEEE Transactions on Multimedia, Volume 26•2024, pp 9070-9082 • https://doi.org/10.1109/TMM.2024.3385676
Emotion recognition is essential in the diagnosis and rehabilitation of various mental diseases. In the last decade, electroencephalogram (EEG)-based emotion recognition has been intensively investigated due to its prominative accuracy and reliability, ...
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Growing Like a Tree: Finding Trunks From Graph Skeleton Trees
- Zhongyu Huang
  Laboratory of Brain Atlas and Brain-Inspired Intelligence, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
  ,
- Yingheng Wang
  Department of Computer Science, Cornell University, Ithaca, NY, USA
  ,
- Chaozhuo Li
  Department of Social Computing, Microsoft Research Asia, Beijing, China
  ,
- Huiguang He
  Laboratory of Brain Atlas and Brain-Inspired Intelligence, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 46, Issue 5•May 2024, pp 2838-2851 • https://doi.org/10.1109/TPAMI.2023.3336315
The message-passing paradigm has served as the foundation of graph neural networks (GNNs) for years, making them achieve great success in a wide range of applications. Despite its elegance, this paradigm presents several unexpected challenges for graph-...
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A Fine-Grained Domain Adaptation Method for Cross-Session Vigilance Estimation in SSVEP-Based BCI
- Kangning Wang
  https://ror.org/012tb2g32Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
  Laboratory of Brain Atlas and Brain-Inspired Intelligence, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
  ,
- Shuang Qiu
  Laboratory of Brain Atlas and Brain-Inspired Intelligence, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
  https://ror.org/05qbk4x57School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
  ,
- Wei Wei
  Laboratory of Brain Atlas and Brain-Inspired Intelligence, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
  ,
- Ying Gao
  Laboratory of Brain Atlas and Brain-Inspired Intelligence, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
  ,
- Huiguang He
  Laboratory of Brain Atlas and Brain-Inspired Intelligence, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
  https://ror.org/05qbk4x57School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
  ,
- Minpeng Xu
  https://ror.org/012tb2g32Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
  https://ror.org/012tb2g32College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
  ,
- Dong Ming
  https://ror.org/012tb2g32Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
  https://ror.org/012tb2g32College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
Neural Information Processing•November 2023, pp 67-80• https://doi.org/10.1007/978-981-99-8067-3_6
Abstract
Brain-computer interface (BCI), a direct communication system between the human brain and external environment, can provide assistance for people with disabilities. Vigilance is an important cognitive state and has a close influence on the ...
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RoBrain: Towards Robust Brain-to-Image Reconstruction via Cross-Domain Contrastive Learning
- Che Liu
  Laboratory of Brain Atlas and Brain-Inspired Intelligence, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Science, Beijing, China
  https://ror.org/05qbk4x57School of Future Technology, University of Chinese Academy of Sciences, Beijing, China
  ,
- Changde Du
  Laboratory of Brain Atlas and Brain-Inspired Intelligence, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Science, Beijing, China
  ,
- Huiguang He
  Laboratory of Brain Atlas and Brain-Inspired Intelligence, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Science, Beijing, China
  https://ror.org/05qbk4x57School of Future Technology, University of Chinese Academy of Sciences, Beijing, China
Neural Information Processing•November 2023, pp 227-238• https://doi.org/10.1007/978-981-99-8067-3_17
Abstract
With the development of neuroimaging technology and deep learning methods, neural decoding with functional Magnetic Resonance Imaging (fMRI) of human brain has attracted more and more attention. Neural reconstruction task, which intends to ...
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Published By ACM
Published By ACM
MindDiffuser: Controlled Image Reconstruction from Human Brain Activity with Semantic and Structural Diffusion
- Yizhuo Lu
  CASIA & University of Chinese Academy of Sciences, Beijing, China
  ,
- Changde Du
  CASIA, Beijing, China
  ,
- Qiongyi Zhou
  CASIA & University of Chinese Academy of Sciences, Beijing, China
  ,
- Dianpeng Wang
  Beijing Institute of Technology, Beijing, China
  ,
- Huiguang He
  CASIA & University of Chinese Academy of Sciences, Beijing, China
MM '23: Proceedings of the 31st ACM International Conference on Multimedia•October 2023, pp 5899-5908• https://doi.org/10.1145/3581783.3613832

Reconstructing visual stimuli from brain recordings has been a meaningful and challenging task. Especially, the achievement of precise and controllable image reconstruction bears great significance in propelling the progress and utilization of brain-...
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Published By ACM
Published By ACM
Auditory Attention Decoding with Task-Related Multi-View Contrastive Learning
- Xiaoyu Chen
  Institute of Automation, Chinese Academy of Sciences & University of Chinese Academy of Science, Beijing, China
  ,
- Changde Du
  Institute of Automation, Chinese Academy of Sciences, Beijing, China
  ,
- Qiongyi Zhou
  Institute of Automation, Chinese Academy of Sciences & University of Chinese Academy of Science, Beijing, China
  ,
- Huiguang He
  Institute of Automation, Chinese Academy of Sciences & University of Chinese Academy of Science, Beijing, China
MM '23: Proceedings of the 31st ACM International Conference on Multimedia•October 2023, pp 6025-6033• https://doi.org/10.1145/3581783.3611869

The human brain can easily focus on one speaker and suppress others in scenarios such as a cocktail party. Recently, researchers found that auditory attention can be decoded from the electroencephalogram (EEG) data. However, most existing deep learning ...
1Citation
502
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Mammo-Net: Integrating Gaze Supervision and Interactive Information in Multi-view Mammogram Classification
- Changkai Ji
  https://ror.org/030bhh786School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
  State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
  ,
- Changde Du
  State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
  ,
- Qing Zhang
  https://ror.org/0220qvk04Department of Radiology, Renji Hospital Shanghai Jiao Tong University School of Medicine, Shanghai, China
  ,
- Sheng Wang
  https://ror.org/030bhh786School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
  https://ror.org/0220qvk04Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
  Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
  ,
- Chong Ma
  https://ror.org/01y0j0j86School of Automation, Northwestern Polytechnical University, Xi’an, China
  ,
- Jiaming Xie
  https://ror.org/02zhqgq86Department of Computer Science, The University of Hong Kong, Hong Kong, China
  ,
- Yan Zhou
  https://ror.org/0220qvk04Department of Radiology, Renji Hospital Shanghai Jiao Tong University School of Medicine, Shanghai, China
  ,
- Huiguang He
  https://ror.org/030bhh786School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
  State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
  ,
- Dinggang Shen
  https://ror.org/030bhh786School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
  Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
  Shanghai Clinical Research and Trial Center, Shanghai, China
Medical Image Computing and Computer Assisted Intervention – MICCAI 2023•October 2023, pp 68-78• https://doi.org/10.1007/978-3-031-43990-2_7
Abstract
Breast cancer diagnosis is a challenging task. Recently, the application of deep learning techniques to breast cancer diagnosis has become a popular trend. However, the effectiveness of deep neural networks is often limited by the lack of ...
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Improved Video Emotion Recognition With Alignment of CNN and Human Brain Representations
- Kaicheng Fu
  Laboratory of Brain Atlas and Brain-Inspired Intelligence, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
  ,
- Changde Du
  Laboratory of Brain Atlas and Brain-Inspired Intelligence, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
  ,
- Shengpei Wang
  Laboratory of Brain Atlas and Brain-Inspired Intelligence, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
  ,
- Huiguang He
  Laboratory of Brain Atlas and Brain-Inspired Intelligence, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
IEEE Transactions on Affective Computing, Volume 15, Issue 3•July-Sept. 2024, pp 1026-1040 • https://doi.org/10.1109/TAFFC.2023.3316173
The ability to perceive emotions is an important criterion for judging whether a machine is intelligent. To this end, a large number of emotion recognition algorithms have been developed especially for visual information such as video. Most previous ...
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Decoding Visual Neural Representations by Multimodal Learning of Brain-Visual-Linguistic Features
- Changde Du
  Research Center for Brain-Inspired Intelligence, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
  ,
- Kaicheng Fu
  Research Center for Brain-Inspired Intelligence, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
  ,
- Jinpeng Li
  Ningbo HwaMei Hospital, UCAS, Zhejiang, China
  ,
- Huiguang He
  Research Center for Brain-Inspired Intelligence, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 45, Issue 9•Sept. 2023, pp 10760-10777 • https://doi.org/10.1109/TPAMI.2023.3263181
Decoding human visual neural representations is a challenging task with great scientific significance in revealing vision-processing mechanisms and developing brain-like intelligent machines. Most existing methods are difficult to generalize to novel ...
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A multimodal approach to estimating vigilance in SSVEP-based BCI
- Kangning Wang
  Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China
  Laboratory of Brain Atlas and Brain-Inspired Intelligence, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
  ,
- Shuang Qiu
  Laboratory of Brain Atlas and Brain-Inspired Intelligence, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
  School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
  ,
- Wei Wei
  Laboratory of Brain Atlas and Brain-Inspired Intelligence, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
  ,
- Yukun Zhang
  Laboratory of Brain Atlas and Brain-Inspired Intelligence, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
  School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
  ,
- Shengpei Wang
  Laboratory of Brain Atlas and Brain-Inspired Intelligence, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
  ,
- Huiguang He
  Laboratory of Brain Atlas and Brain-Inspired Intelligence, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
  School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
  ,
- Minpeng Xu
  Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China
  College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China
  ,
- Tzyy-Ping Jung
  Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China
  College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China
  Swartz Center for Computational Neuroscience, University of California, San Diego, CA 92093, USA
  ,
- Dong Ming
  Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China
  College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China
Expert Systems with Applications: An International Journal, Volume 225, Issue C•Sep 2023 • https://doi.org/10.1016/j.eswa.2023.120177
Abstract
Brain-computer interface (BCI) is a communication system that allows a direct connection between the human brain and external devices, which is able to provide assistance and improve the quality of life for people with disabilities. Vigilance is ...
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Cross-modal guiding and reweighting network for multi-modal RSVP-based target detection
- Jiayu Mao
  Laboratory of Brain Atlas and Brain-Inspired Intelligence, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
  School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
  ,
- Shuang Qiu
  Laboratory of Brain Atlas and Brain-Inspired Intelligence, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
  School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
  ,
- Wei Wei
  Laboratory of Brain Atlas and Brain-Inspired Intelligence, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
  ,
- Huiguang He
  Laboratory of Brain Atlas and Brain-Inspired Intelligence, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
  School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
Neural Networks, Volume 161, Issue C•Apr 2023, pp 65-82 • https://doi.org/10.1016/j.neunet.2023.01.009
Abstract
Rapid Serial Visual Presentation (RSVP) based Brain–Computer Interface (BCI) facilities the high-throughput detection of rare target images by detecting evoked event-related potentials (ERPs). At present, the decoding accuracy of the ...
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We design and conduct RSVP experiments to collect EEG and eye movements data.
A ...
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A Zero-Training Method for RSVP-Based Brain Computer Interface
- Xujin Li
  Research Center for Brain-Inspired Intelligence, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
  University of Chinese Academy of Sciences, Beijing, China
  ,
- Shuang Qiu
  Research Center for Brain-Inspired Intelligence, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
  University of Chinese Academy of Sciences, Beijing, China
  ,
- Wei Wei
  Research Center for Brain-Inspired Intelligence, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
  ,
- Huiguang He
  Research Center for Brain-Inspired Intelligence, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
  University of Chinese Academy of Sciences, Beijing, China
  Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Beijing, China
Pattern Recognition and Computer Vision•October 2022, pp 113-125• https://doi.org/10.1007/978-3-031-18910-4_10
Abstract
Brain-Computer Interface (BCI) is a communication system that transmits information between the brain and the outside world which does not rely on peripheral nerves and muscles. Rapid Serial Visual Presentation (RSVP)-based BCI system is an ...
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VigilanceNet: Decouple Intra- and Inter-Modality Learning for Multimodal Vigilance Estimation in RSVP-Based BCI
- Xinyu Cheng
  CASIA & University of CAS, Beijing, China
  ,
- Wei Wei
  CASIA, Beijing, China
  ,
- Changde Du
  CASIA, Beijing, China
  ,
- Shuang Qiu
  CASIA, Beijing, China
  ,
- Sanli Tian
  University of CAS, Beijing, China
  ,
- Xiaojun Ma
  CASIA & University of CAS, Beijing, China
  ,
- Huiguang He
  CASIA & University of CAS, Beijing, China
MM '22: Proceedings of the 30th ACM International Conference on Multimedia•October 2022, pp 209-217• https://doi.org/10.1145/3503161.3548367

Recently, brain-computer interface (BCI) technology has made impressive progress and has been developed for many applications. Thereinto, the BCI system based on rapid serial visual presentation (RSVP) is a promising information detection technology. ...
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TFF-Former: Temporal-Frequency Fusion Transformer for Zero-training Decoding of Two BCI Tasks
- Xujin Li
  CASIA & University of Chinese Academy of Sciences, Beijing, China
  ,
- Wei Wei
  CASIA, Beijing, China
  ,
- Shuang Qiu
  CASIA & University of Chinese Academy of Sciences, Beijing, China
  ,
- Huiguang He
  CASIA & University of Chinese Academy of Sciences, Beijing, China
MM '22: Proceedings of the 30th ACM International Conference on Multimedia•October 2022, pp 51-59• https://doi.org/10.1145/3503161.3548269

Brain-computer interface (BCI) systems provide a direct connection between the human brain and external devices. Visual evoked BCI systems including Event-related Potential (ERP) and Steady-state Visual Evoked Potential (SSVEP) have attracted extensive ...
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Graph Emotion Decoding from Visually Evoked Neural Responses
- Zhongyu Huang
  Research Center for Brain-Inspired Intelligence, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
  School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
  ,
- Changde Du
  Research Center for Brain-Inspired Intelligence, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
  School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
  ,
- Yingheng Wang
  Department of Electronic Engineering, Tsinghua University, Beijing, China
  Department of Biomedical Engineering, Johns Hopkins University, Baltimore, USA
  ,
- Huiguang He
  Research Center for Brain-Inspired Intelligence, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
  School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
  Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Beijing, China
Medical Image Computing and Computer Assisted Intervention – MICCAI 2022•September 2022, pp 396-405• https://doi.org/10.1007/978-3-031-16452-1_38
Abstract
Brain signal-based affective computing has recently drawn considerable attention due to its potential widespread applications. Most existing efforts exploit emotion similarities or brain region similarities to learn emotion representations. ...
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research-article
Deep Modality Assistance Co-Training Network for Semi-Supervised Multi-Label Semantic Decoding
- Dan Li
  Research Center for Brain-Inspired Intelligence, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
  ,
- Changde Du
  Research Center for Brain-Inspired Intelligence, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
  ,
- Haibao Wang
  Research Center for Brain-Inspired Intelligence, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
  ,
- Qiongyi Zhou
  Research Center for Brain-Inspired Intelligence, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
  ,
- Huiguang He
  Research Center for Brain-Inspired Intelligence, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
IEEE Transactions on Multimedia, Volume 24•2022, pp 3287-3299 • https://doi.org/10.1109/TMM.2021.3104980
Multi-label semantic decoding is a challenging task with great scientific significance and application value. The existing methods mainly focus on label learning and ignore the amount of information contained in the sample itself, especially non-image ...
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SNAP: Shaping neural architectures progressively via information density criterion
- Zhiqiang Chen
  Research Center for Brain-Inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China
  University of Chinese Academy of Sciences, Beijing, China
  ,
- Ting-Bing Xu
  University of Chinese Academy of Sciences, Beijing, China
  National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
  ,
- Weijian Liao
  Faculty of Electronic and Information Engineering, Xi’an Jiaotong University, Xian, China
  ,
- Zhengcheng Li
  Faculty of Electronic and Information Engineering, Xi’an Jiaotong University, Xian, China
  ,
- Jinpeng Li
  Research Center for Brain-Inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China
  Ningbo Hwa Mei Hospital, University of Chinese Academy of Sciences, Zhejiang, China
  ,
- Cheng-Lin Liu
  University of Chinese Academy of Sciences, Beijing, China
  National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
  Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Beijing, China
  ,
- Huiguang He
  Research Center for Brain-Inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China
  University of Chinese Academy of Sciences, Beijing, China
  National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
  Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Beijing, China
Pattern Recognition, Volume 116, Issue C•Aug 2021 • https://doi.org/10.1016/j.patcog.2021.107923
Highlights

We propose a progressive method SNAP to shape a given neural architecture to a more reasonable one progressively, which is inspired by the streamline of ...
Abstract
Excellent neural network architecture is built on the specific target task and device. As the target task or device is different, the neural architecture we need will be different, too. Rather than redesigning or searching a brand new ...
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Semi-supervised cross-modal image generation with generative adversarial networks
- Dan Li
  Research Center for Brain-inspired Intelligence and National Laboratory of Pattern Recognition, Institute of Automation Chinese Academy of Sciences, Beijing 100190, China
  University of Chinese Academy of Sciences, Beijing 100190, China
  ,
- Changde Du
  Research Center for Brain-inspired Intelligence and National Laboratory of Pattern Recognition, Institute of Automation Chinese Academy of Sciences, Beijing 100190, China
  University of Chinese Academy of Sciences, Beijing 100190, China
  ,
- Huiguang He
  Research Center for Brain-inspired Intelligence and National Laboratory of Pattern Recognition, Institute of Automation Chinese Academy of Sciences, Beijing 100190, China
  University of Chinese Academy of Sciences, Beijing 100190, China
  Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Beijing 100190, China
Pattern Recognition, Volume 100, Issue C•Apr 2020 • https://doi.org/10.1016/j.patcog.2019.107085
Abstract
Cross-modal image generation is an important aspect of the multi-modal learning. Existing methods usually use the semantic feature to reduce the modality gap. Although these methods have achieved notable progress, there are still some ...
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Author Profile Pages

Description: The Author Profile Page initially collects all the professional information known about authors from the publications record as known by the ACM bibliographic database, the Guide. Coverage of ACM publications is comprehensive from the 1950's. Coverage of other publishers generally starts in the mid 1980's. The Author Profile Page supplies a quick snapshot of an author's contribution to the field and some rudimentary measures of influence upon it. Over time, the contents of the Author Profile page may expand at the direction of the community.
Please see the following 2007 Turing Award winners' profiles as examples:
History: Disambiguation of author names is of course required for precise identification of all the works, and only those works, by a unique individual. Of equal importance to ACM, author name normalization is also one critical prerequisite to building accurate citation and download statistics. For the past several years, ACM has worked to normalize author names, expand reference capture, and gather detailed usage statistics, all intended to provide the community with a robust set of publication metrics. The Author Profile Pages reveal the first result of these efforts.
Normalization: ACM uses normalization algorithms to weigh several types of evidence for merging and splitting names.
These include:
- co-authors: if we have two names and cannot disambiguate them based on name alone, then we see if they have a co-author in common. If so, this weighs towards the two names being the same person.
- affiliations: names in common with same affiliation weighs toward the two names being the same person.
- publication title: names in common whose works are published in same journal weighs toward the two names being the same person.
- keywords: names in common whose works address the same subject matter as determined from title and keywords, weigh toward being the same person.
The more conservative the merging algorithms, the more bits of evidence are required before a merge is made, resulting in greater precision but lower recall of works for a given Author Profile. Many bibliographic records have only author initials. Many names lack affiliations. With very common family names, typical in Asia, more liberal algorithms result in mistaken merges.

Automatic normalization of author names is not exact. Hence it is clear that manual intervention based on human knowledge is required to perfect algorithmic results. ACM is meeting this challenge, continuing to work to improve the automated merges by tweaking the weighting of the evidence in light of experience.
Bibliometrics: In 1926, Alfred Lotka formulated his power law (known as Lotka's Law) describing the frequency of publication by authors in a given field. According to this bibliometric law of scientific productivity, only a very small percentage (~6%) of authors in a field will produce more than 10 articles while the majority (perhaps 60%) will have but a single article published. With ACM's first cut at author name normalization in place, the distribution of our authors with 1, 2, 3..n publications does not match Lotka's Law precisely, but neither is the distribution curve far off. For a definition of ACM's first set of publication statistics, see Bibliometrics
Future Direction: The initial release of the Author Edit Screen is open to anyone in the community with an ACM account, but it is limited to personal information. An author's photograph, a Home Page URL, and an email may be added, deleted or edited. Changes are reviewed before they are made available on the live site.
ACM will expand this edit facility to accommodate more types of data and facilitate ease of community participation with appropriate safeguards. In particular, authors or members of the community will be able to indicate works in their profile that do not belong there and merge others that do belong but are currently missing.

A direct search interface for Author Profiles will be built.

An institutional view of works emerging from their faculty and researchers will be provided along with a relevant set of metrics.

It is possible, too, that the Author Profile page may evolve to allow interested authors to upload unpublished professional materials to an area available for search and free educational use, but distinct from the ACM Digital Library proper. It is hard to predict what shape such an area for user-generated content may take, but it carries interesting potential for input from the community.

Bibliometrics

The ACM DL is a comprehensive repository of publications from the entire field of computing.

It is ACM's intention to make the derivation of any publication statistics it generates clear to the user.

Average citations per article = The total Citation Count divided by the total Publication Count.
Citation Count = cumulative total number of times all authored works by this author were cited by other works within ACM's bibliographic database. Almost all reference lists in articles published by ACM have been captured. References lists from other publishers are less well-represented in the database. Unresolved references are not included in the Citation Count. The Citation Count is citations TO any type of work, but the references counted are only FROM journal and proceedings articles. Reference lists from books, dissertations, and technical reports have not generally been captured in the database. (Citation Counts for individual works are displayed with the individual record listed on the Author Page.)
Publication Count = all works of any genre within the universe of ACM's bibliographic database of computing literature of which this person was an author. Works where the person has role as editor, advisor, chair, etc. are listed on the page but are not part of the Publication Count.
Publication Years = the span from the earliest year of publication on a work by this author to the most recent year of publication of a work by this author captured within the ACM bibliographic database of computing literature (The ACM Guide to Computing Literature, also known as "the Guide".
Available for download = the total number of works by this author whose full texts may be downloaded from an ACM full-text article server. Downloads from external full-text sources linked to from within the ACM bibliographic space are not counted as 'available for download'.
Average downloads per article = The total number of cumulative downloads divided by the number of articles (including multimedia objects) available for download from ACM's servers.
Downloads (cumulative) = The cumulative number of times all works by this author have been downloaded from an ACM full-text article server since the downloads were first counted in May 2003. The counts displayed are updated monthly and are therefore 0-31 days behind the current date. Robotic activity is scrubbed from the download statistics.
Downloads (12 months) = The cumulative number of times all works by this author have been downloaded from an ACM full-text article server over the last 12-month period for which statistics are available. The counts displayed are usually 1-2 weeks behind the current date. (12-month download counts for individual works are displayed with the individual record.)
Downloads (6 weeks) = The cumulative number of times all works by this author have been downloaded from an ACM full-text article server over the last 6-week period for which statistics are available. The counts displayed are usually 1-2 weeks behind the current date. (6-week download counts for individual works are displayed with the individual record.)

ACM Author-Izer Service

Summary Description

ACM Author-Izer is a unique service that enables ACM authors to generate and post links on both their homepage and institutional repository for visitors to download the definitive version of their articles from the ACM Digital Library at no charge.

Downloads from these sites are captured in official ACM statistics, improving the accuracy of usage and impact measurements. Consistently linking to definitive version of ACM articles should reduce user confusion over article versioning.

ACM Author-Izer also extends ACM’s reputation as an innovative “Green Path” publisher, making ACM one of the first publishers of scholarly works to offer this model to its authors.

To access ACM Author-Izer, authors need to establish a free ACM web account. Should authors change institutions or sites, they can utilize the new ACM service to disable old links and re-authorize new links for free downloads from a different site.

How ACM Author-Izer Works

Authors may post ACM Author-Izer links in their own bibliographies maintained on their website and their own institution’s repository. The links take visitors to your page directly to the definitive version of individual articles inside the ACM Digital Library to download these articles for free.

The Service can be applied to all the articles you have ever published with ACM.

Depending on your previous activities within the ACM DL, you may need to take up to three steps to use ACM Author-Izer.

For authors who do not have a free ACM Web Account:

Go to the ACM DL http://dl.acm.org/ and click SIGN UP. Once your account is established, proceed to next step.

For authors who have an ACM web account, but have not edited their ACM Author Profile page:

Sign in to your ACM web account and go to your Author Profile page. Click "Add personal information" and add photograph, homepage address, etc. Click ADD AUTHOR INFORMATION to submit change. Once you receive email notification that your changes were accepted, you may utilize ACM Author-izer.

For authors who have an account and have already edited their Profile Page:

Sign in to your ACM web account, go to your Author Profile page in the Digital Library, look for the ACM Author-izer link below each ACM published article, and begin the authorization process. If you have published many ACM articles, you may find a batch Authorization process useful. It is labeled: "Export as: ACM Author-Izer Service"

ACM Author-Izer also provides code snippets for authors to display download and citation statistics for each “authorized” article on their personal pages. Downloads from these pages are captured in official ACM statistics, improving the accuracy of usage and impact measurements. Consistently linking to the definitive version of ACM articles should reduce user confusion over article versioning.

Note: You still retain the right to post your author-prepared preprint versions on your home pages and in your institutional repositories with DOI pointers to the definitive version permanently maintained in the ACM Digital Library. But any download of your preprint versions will not be counted in ACM usage statistics. If you use these AUTHOR-IZER links instead, usage by visitors to your page will be recorded in the ACM Digital Library and displayed on your page.

FAQ

Q. What is ACM Author-Izer?
A. ACM Author-Izer is a unique, link-based, self-archiving service that enables ACM authors to generate and post links on either their home page or institutional repository for visitors to download the definitive version of their articles for free.
Q. What articles are eligible for ACM Author-Izer?
A. ACM Author-Izer can be applied to all the articles authors have ever published with ACM. It is also available to authors who will have articles published in ACM publications in the future.

Q. Are there any restrictions on authors to use this service?
A. No. An author does not need to subscribe to the ACM Digital Library nor even be a member of ACM.

Q. What are the requirements to use this service?
A. To access ACM Author-Izer, authors need to have a free ACM web account, must have an ACM Author Profile page in the Digital Library, and must take ownership of their Author Profile page.

Q. What is an ACM Author Profile Page?
A. The Author Profile Page initially collects all the professional information known about authors from the publications record as known by the ACM Digital Library. The Author Profile Page supplies a quick snapshot of an author's contribution to the field and some rudimentary measures of influence upon it. Over time, the contents of the Author Profile page may expand at the direction of the community. Please visit the ACM Author Profile documentation page for more background information on these pages.

Q. How do I find my Author Profile page and take ownership?
A. You will need to take the following steps:
- Create a free ACM Web Account
- Sign-In to the ACM Digital Library
- Find your Author Profile Page by searching the ACM Digital Library for your name
- Find the result you authored (where your author name is a clickable link)
- Click on your name to go to the Author Profile Page
- Click the "Add Personal Information" link on the Author Profile Page
- Wait for ACM review and approval; generally less than 24 hours

Q. Why does my photo not appear?
A. Make sure that the image you submit is in .jpg or .gif format and that the file name does not contain special characters

Q. What if I cannot find the Add Personal Information function on my author page?
A. The ACM account linked to your profile page is different than the one you are logged into. Please logout and login to the account associated with your Author Profile Page.

Q. What happens if an author changes the location of his bibliography or moves to a new institution?
A. Should authors change institutions or sites, they can utilize ACM Author-Izer to disable old links and re-authorize new links for free downloads from a new location.

Q. What happens if an author provides a URL that redirects to the author’s personal bibliography page?
A. The service will not provide a free download from the ACM Digital Library. Instead the person who uses that link will simply go to the Citation Page for that article in the ACM Digital Library where the article may be accessed under the usual subscription rules.

However, if the author provides the target page URL, any link that redirects to that target page will enable a free download from the Service.

Q. What happens if the author’s bibliography lives on a page with several aliases?
A. Only one alias will work, whichever one is registered as the page containing the author’s bibliography. ACM has no technical solution to this problem at this time.

Q. Why should authors use ACM Author-Izer?
A. ACM Author-Izer lets visitors to authors’ personal home pages download articles for no charge from the ACM Digital Library. It allows authors to dynamically display real-time download and citation statistics for each “authorized” article on their personal site.

Q. Does ACM Author-Izer provide benefits for authors?
A. Downloads of definitive articles via Author-Izer links on the authors’ personal web page are captured in official ACM statistics to more accurately reflect usage and impact measurements.

Authors who do not use ACM Author-Izer links will not have downloads from their local, personal bibliographies counted. They do, however, retain the existing right to post author-prepared preprint versions on their home pages or institutional repositories with DOI pointers to the definitive version permanently maintained in the ACM Digital Library.

Q. How does ACM Author-Izer benefit the computing community?
A. ACM Author-Izer expands the visibility and dissemination of the definitive version of ACM articles. It is based on ACM’s strong belief that the computing community should have the widest possible access to the definitive versions of scholarly literature. By linking authors’ personal bibliography with the ACM Digital Library, user confusion over article versioning should be reduced over time.

In making ACM Author-Izer a free service to both authors and visitors to their websites, ACM is emphasizing its continuing commitment to the interests of its authors and to the computing community in ways that are consistent with its existing subscription-based access model.

Q. How do authors access ACM Author-Izer?
A. See How to Use

Q. Why can’t I find my most recent publication in my ACM Author Profile Page?
A. There is a time delay between publication and the process which associates that publication with an Author Profile Page. Right now, that process usually takes 4-8 weeks.

Q. How does ACM Author-Izer expand ACM’s “Green Path” Access Policies?
A. ACM Author-Izer extends the rights and permissions that authors retain even after copyright transfer to ACM, which has been among the “greenest” publishers. ACM enables its author community to retain a wide range of rights related to copyright and reuse of materials. They include:
- Posting rights that ensure free access to their work outside the ACM Digital Library and print publications
- Rights to reuse any portion of their work in new works that they may create
- Copyright to artistic images in ACM’s graphics-oriented publications that authors may want to exploit in commercial contexts
- All patent rights, which remain with the original owner

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Contrastive fine-grained domain adaptation network for EEG-based vigilance estimation

Identifying the hierarchical emotional areas in the human brain through information fusion

PGCN: Pyramidal Graph Convolutional Network for EEG Emotion Recognition

Growing Like a Tree: Finding Trunks From Graph Skeleton Trees

A Fine-Grained Domain Adaptation Method for Cross-Session Vigilance Estimation in SSVEP-Based BCI

RoBrain: Towards Robust Brain-to-Image Reconstruction via Cross-Domain Contrastive Learning

MindDiffuser: Controlled Image Reconstruction from Human Brain Activity with Semantic and Structural Diffusion

Auditory Attention Decoding with Task-Related Multi-View Contrastive Learning

Mammo-Net: Integrating Gaze Supervision and Interactive Information in Multi-view Mammogram Classification

Improved Video Emotion Recognition With Alignment of CNN and Human Brain Representations

Decoding Visual Neural Representations by Multimodal Learning of Brain-Visual-Linguistic Features

A multimodal approach to estimating vigilance in SSVEP-based BCI

Cross-modal guiding and reweighting network for multi-modal RSVP-based target detection

A Zero-Training Method for RSVP-Based Brain Computer Interface

VigilanceNet: Decouple Intra- and Inter-Modality Learning for Multimodal Vigilance Estimation in RSVP-Based BCI

TFF-Former: Temporal-Frequency Fusion Transformer for Zero-training Decoding of Two BCI Tasks

Graph Emotion Decoding from Visually Evoked Neural Responses

Deep Modality Assistance Co-Training Network for Semi-Supervised Multi-Label Semantic Decoding

SNAP: Shaping neural architectures progressively via information density criterion

Semi-supervised cross-modal image generation with generative adversarial networks