Chuncheng Zhang
Zhang Chuncheng, associate researcher at the Institute of Automation, Chinese Academy of Sciences, is engaged in research in the fields of artificial intelligence, neural coding and decoding, multimodal neural computing, and brain-computer interface, and has published many research papers. He presided over the Beijing Natural Science Foundation Youth Fund Project, and participated in the Science and Technology Innovation 2030-""Brain Science and Brain-like Research"" major project as a research and development backbone, the National Natural Science Foundation Key Project, and the Ministry of Science and Technology National Key Research and Development Project.
Educational experience: (1) 2012-09 to 2018-06, Beijing Normal University, Computer Applications, PhD; the work content in the past five years has focused on the research of neural information decoding and brain-computer interface systems, and improved the decodable range and applicable scenarios of brain-computer interface systems. In daily life, the brain perceives the surrounding environment and forms decisions. This process corresponds to specific neural activities. The brain-computer interface system assists people by reasonably controlling peripheral devices based on neural information decoding. In view of the low intensity of neural signals in brain-computer interface systems and the high dependence of decoding performance on specific experimental paradigms, the decodability of signals is improved through the research of paradigm design, decoding methods and feedback systems, and the rationality of the design of converting decoding results into control instructions is improved, so as to improve the response speed and application scope of brain-computer interface systems at key nodes. (2) 2005-09 to 2009-06, Tianjin University, Electronic Information Engineering, Bachelor
Postdoctoral work experience: (1) 2018-06 to 2020-06, Institute of Automation
Research and academic work experience: (1) 2022-04 to present, Institute of Automation, Chinese Academy of Sciences, Associate Researcher (2) 2020-06 to 2022-04, Institute of Automation, Chinese Academy of Sciences, Assistant Researcher
Cognitive Neuroscience, Brain-Computer Interface (BCI)
1. Beijing Natural Science Foundation, Youth Project, 4214078: Research on Visual Neural Encoding and Decoding of Occluded Objects Based on Functional Magnetic Resonance Imaging, from January 2021 to December 2022, 100,000 RMB, completed, Principal Investigator.
2. National Natural Science Foundation of China, Joint Fund Project, U21A20388: Research on New Brain-Computer Interface System Principles and Methods Based on Atomic Magnetometer Magnetoencephalography, from January 1, 2022, to December 31, 2025, 2.6 million RMB, ongoing, participant.
3. National Natural Science Foundation of China, General Project, 61976209: Semantic Decoding of Brain Activity and Visual Reconstruction Stimulated by Natural Scenes, from January 1, 2020, to December 31, 2023, 610,000 RMB, completed, participant.
4. National Natural Science Foundation of China, General Project, 62276262: Research on Key Technologies of Closed-Loop Repetitive Transcranial Magnetic Stimulation for Motor Function Rehabilitation after Stroke, from January 1, 2023, to December 31, 2026, 530,000 RMB, ongoing, participant.
1. Chuncheng Zhang; Li Yao; Sutao Song; Xiaotong Wen; Xiaojie Zhao; Zhiying Long ; Euler Elastica Regularized Logistic Regression for Whole-Brain Decoding of fMRI Data, IEEE Transactions on Biomedical Engineering, 2018, 65(7): 1639-1653
2. Chuncheng Zhang; Sutao Song; Xiaotong Wen; Li Yao; Zhiying Long ; Improved sparse decomposition based on a smoothed L0 norm using a Laplacian kernel to select features from fMRI data, Journal of Neuroscience Methods, 2014, 245(0): 15-24
3. Yuan Li; Chuncheng Zhang; Chunping Hou; Li Yao; Jiacai Zhang; Zhiying Long ; Stereoscopic processing of crossed and uncrossed disparities in the human visual cortex, BMC Neuroscience, 2017, 18(80): 1-16
4. Yuan Li; Chunping Hou; Li Yao; Chuncheng Zhang; Hongna Zheng; Jiacai Zhang; Zhiying Long ; Disparity level identification using the voxel-wise Gabor model, disparity, fMRI, Gabor, identify, voxel-wise encoding model, 2019, 0(0): 1-15
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