Zheng Li, Ph.D

Affiliations: 
2013-2019 State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University, Beijing, Beijing Shi, China 
Area:
Brain-Computer-Interface
Website:
http://brain.bnu.edu.cn/a/zh/keyantuandui/fujiaoshou_fuyanjiuyuan/2016/0222/602.html
Google:
"Zheng Li"
Bio:

Research Interests
My main research interests are in brain-machine interface (brain-computer interface) technologies and related neuroscience questions. Particularly, I am interested in decoding algorithms and signal processing methods for implanted electrode motor-cortical brain-machine interfaces. I also want to build improved models of neural encoding for intended upper-limb movements and better understand the process of learning to use a brain-machine interface. More broadly, I am also interested in decoding for other neural interface modalities, particularly functional near infrared spectroscopy.

Educational Background
2004/08 - 2010/05, Duke University, Ph.D. in Computer Science, Advisor: Craig S. Henriquez
2001/01 - 2004/05, Purdue University, B.S. in Computer Science and Mathematics

Employment History
2013/03 - current, Beijing Normal University, School of Brain and Cognitive Sciences, Associate Research Scientist
2011/03 - 2012/12, Duke University, Postdoctoral Associate, Advisor: Miguel A. L. Nicolelis
2010/06 - 2011/02, Duke University, Research Scholar, Advisor: Miguel A. L. Nicolelis
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Parents

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Craig S. Henriquez grad student 2004-2010 Duke (BME Tree)
Miguel A. Nicolelis post-doc 2011-2012 Duke
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Publications

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Jiang Y, Li Z, Zhao Y, et al. (2020) Targeting brain functions from the scalp: Transcranial brain atlas based on large-scale fMRI data synthesis. Neuroimage. 116550
Li J, Chen X, Li Z. (2019) Spike detection and spike sorting with a hidden Markov model improves offline decoding of motor cortical recordings. Journal of Neural Engineering. 16: 016014
Xiao X, Yu X, Zhang Z, et al. (2018) Transcranial brain atlas. Science Advances. 4: eaar6904
Li J, Li Z. (2017) Sums of Spike Waveform Features for Motor Decoding. Frontiers in Neuroscience. 11: 406
Li Z, Jiang YH, Duan L, et al. (2017) A Gaussian mixture model based adaptive classifier for fNIRS brain-computer interfaces and its testing via simulation. Journal of Neural Engineering. 14: 046014
Xiao X, Zhu H, Liu WJ, et al. (2017) Semi-automatic 10/20 Identification Method for MRI-Free Probe Placement in Transcranial Brain Mapping Techniques. Frontiers in Neuroscience. 11: 4
Li S, Li J, Li Z. (2016) An Improved Unscented Kalman Filter Based Decoder for Cortical Brain-Machine Interfaces. Frontiers in Neuroscience. 10: 587
Duan L, Dai RN, Xiao X, et al. (2015) Cluster imaging of multi-brain networks (CIMBN): a general framework for hyperscanning and modeling a group of interacting brains. Frontiers in Neuroscience. 9: 267
Li Z. (2014) Decoding methods for neural prostheses: where have we reached? Frontiers in Systems Neuroscience. 8: 129-129
Schwarz DA, Lebedev MA, Hanson TL, et al. (2014) Chronic, wireless recordings of large-scale brain activity in freely moving rhesus monkeys. Nature Methods. 11: 670-6
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