Zheng Li, Ph.D

2013-2019 State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University, Beijing, Beijing Shi, China 
"Zheng Li"

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
(Show less)

Mean distance: (not calculated yet)


Sign in to add mentor
Craig S. Henriquez grad student 2004-2010 Duke (BME Tree)
Miguel A. Nicolelis post-doc 2011-2012 Duke
BETA: Related publications


You can help our author matching system! If you notice any publications incorrectly attributed to this author, please sign in and mark matches as correct or incorrect.

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
See more...