LIU Quanying
Assistant Professor

For anyone who is interested in joining NCC lab, please feel free to email me.


2013-2017 PhD in Biomedical Engineering, ETH Zurich, Switzerland (Doctoral thesis: “Brain Network Imaging based on High-density Electroencephalography”. Supervisors: Dr. Nicole Wenderoth and Dr. Dante Mantini)
2010-2013 Master in Computer Science, Lanzhou University, China
2006-2010 Undergraduate in Electrical Engineering, Lanzhou University, China


Academic Positions
2019-present Assistant Professor in Department of Biomedical Engineering, Southern University of Science and Technology (PI of Neural Computing & Control lab)
2017-2019 Postdoctoral Scholar in Department of Computing and Mathematical Sciences (CMS), California Institute of Technology (Principal Investigator: Dr. John Doyle)
2017-2019 Independent researcher in Neurosciences, Huntington Medical Research Institute, US
2016-2017 Visiting Scholar in Research Center for Motor Control and Neuroplasticity, KU Leuven, Belgium
2016.10 Late-Summer School on Non-Invasive Brain Stimulation, University Medical Center Freiburg, Germany
2014-2015 Visiting Scholar in Department of Experimental Psychology, University of Oxford, UK


Grants and Fellowships
2018-2021 FWO Postdoctoral Fellowship (3 years)
2017-2019 Boswell Postdoctoral Fellowship ($54,075+$7,000 per annum for 2 years)
2015-2017 Swiss National Science Foundation for Doc.Mobility grant (45,500 CHF for one year)
2014-2015 Swiss National Science Foundation for Mobility grant (19,510 CHF for one year)


Awards and Scholarships
AAIC travel award (2019);
Estes Stars Award (2018);
Excellent Graduate Student Scholarship in China (2013);
IBM China Scholarship (2013);


Journal Publications (including pre-print)

1) Samogin J*, Liu Q*, Marino M, Wenderoth N, Mantini D. (2019), Shared and connection-specific intrinsic interactions in the default mode network, NeuroImage 200 (2019) 474–481 (* equal contribution)

2) Liu Q, Farahibozorg S, Porcaro C, Wenderoth N and Mantini D (2017). Detecting large-scale networks in the human brain using high-density electroencephalography. Human Brain Mapping. 38 (9), 4631-4643
3) Marino M*, Liu Q*, Samogin J, Tecchio F, Mantini D, Porcaro C (2019). Neuronal dynamics enable the functional differentiation of resting state networks in the human brain, Human Brain Mapping, https://doi.org/10.1002/hbm.24458. (* equal contribution)
4) Nakahira Y*, Liu Q*, Sejnowski T, Doyle J.C. (2019), Fitts’ Law for speed-accuracy trade-off is a diversity sweet spot in sensorimotor control, https://arxiv.org/pdf/1906.00905.pdf (* equal contribution)
5) Liu Q, Nakahira Y, Mohideen A, Dai A, Choi S.H, Pan A, Ho D.M, Doyle J.C. (2019), WheelCon: A wheel control-based gaming platform for studying human sensorimotor control, https://arxiv.org/abs/1811.00738 (arxiv)
6) Ruddy K.L, Balsters J, Mantini D, Liu Q, Kassraian-Fard P, Enz N, Mihelj E, Chander B, Soekadar S.R., Wenderoth N (2018), A different state of mind: neural activity related to volitionally up-versus downregulating cortical excitability, eLife, https://elifesciences.org/articles/40843
7) Liu Q, Ganzetti M, Wenderoth N, Mantini D, (2018). Detecting large-scale brain networks using EEG: impact of electrode density, head modelling and source localization, Frontiers in Neuroinformatics 12 (4)
8) Wu H, Feng C, Lu X, Liu X, Liu Q#, (2018). Oxytocin effects on the resting-state mentalizing brain network, https://www.biorxiv.org/content/early/2018/11/08/465658 (# corresponding author, biorxiv)
9) Liu Q, Balsters JH, Baechinger M, van der Groen O, Wenderoth N and Mantini D (2015). Estimating a neutral reference for electroencephalographic recordings: the importance of using a high-density montage and a realistic head model. Journal of Neural Engineering 12(5): 056012.
10) Wei S, Liu Q, Harrington M.G, Sun J, Wu H, Liu X (2019). Nonconformist tendencies related to risky choices in female methamphetamine abstainers, The American journal of drug and alcohol abuse, 1-10

11) Zhao Q, Li H, Hu B#, Li Y, Gillebert C, Mantini D, Liu Q# (2017). Neural correlates of drug-related attentional bias in heroin dependence. Frontiers in Human Neuroscience. DOI: 10.3389/fnhum.2017.00646 (# corresponding author)
12) Zhao Q, Li H, Hu B#, Wu H, Liu Q# (2017). Abstinent heroin addicts tend to take risks: ERP and source localization. Frontiers in Neuroscience 11, 681 (# corresponding author)
13) Zhao Q, Jiang H, Hu B#, Li Y, Zhong N, Li M, Lin W and Liu Q# (2017). Nonlinear Dynamic complexity and Sources of Resting-state EEG in Abstinent Heroin Addicts. IEEE Transactions on NanoBioscience. DOI: 10.1109/TNB.2017.2705689 (# corresponding author)
14) Marino M, Liu Q, Koudelka V, Porcaro C, Hlinka J, Wenderoth N, Mantini D (2018). Adaptive optimal basis set for BCG artifact removal in simultaneous EEG-fMRI, Scientific Reports (1), 8902
15) Marino M, Liu Q, Del Castello M, Corsi C, Wenderoth N, Mantini D (2018). Heart–Brain Interactions in the MR Environment: Characterization of the Ballistocardiogram in EEG Signals Collected During Simultaneous fMRI, Brain topography 31 (3), 337-345

16) Ganzetti M, Liu Q, Mantini D, (2018). A Spatial Registration Toolbox for Structural MR Imaging of the Aging Brain, Neuroinformatics, https://doi.org/10.1007/s12021-018-9355-3
17) Bächinger M, Zerbi V, Moisa M, Polania R, Liu Q, Mantini D, Ruff C and Wenderoth N (2017). Concurrent tACS-fMRI reveals causal influence of power synchronized neural activity on resting state fMRI connectivity. Journal of Neuroscience 1756-16
18) Cao J, Liu Q, Li Y, Yang J, Gu R, Liang J, Qi Y, Wu H, Liu X (2017). Cognitive behavioural therapy attenuates the enhanced early facial stimuli processing in social anxiety disorders: an ERP investigation. Behavioral and Brain Functions 13 (1), 12
19) Marino M, Liu Q, Brem S, Wenderoth N and Mantini D (2016). Automated detection and labelling of high-density EEG electrodes from structural MR images. Journal of Neural Engineering 13(15): 056003
20) Li X, Zhao Q, Liu L, Peng H, Qi Y, Mao C, Fang Z, Liu Q and Hu B. (2010). Improve Affective Learning with EEG Approach. Computing and Informatics 29(4): 557-570.