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刘泉影

助理教授

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刘泉影博士的研究主要集中于多模态脑成像及脑网络算法、神经计算模型、类脑计算、神经反馈控制等领域,属于跨学科的前沿交叉研究,包括计算机科学和自动控制等学科的理论与算法,及其在神经科学、心理学和生物医学等方向的应用。刘泉影博士将高通道脑电(high-density EEG)与核磁脑影像(fMRI)相结合,提出基于精准脑模型的脑源定位算法和脑网络分析算法;将反馈控制理论用于感知觉运动系统研究,提出了人脑‘分层架构’理论与‘Diversity-enabled Sweet Spots’理论;并将计算建模与神经信号相结合用以研究大脑的计算机制。 刘泉影博士获得瑞士科学基金、美国Boswell基金和比利时FWO基金的资助,取得了一系列突出的研究成果,在eLife、Neuroimage、Human Brain Mapping、Journal of Neuroscience、Journal of neural engineering等国际权威期刊共发表论文17篇,American Control Conference等顶级会议论文12篇。其发表的论文被引用超过350次。

 

刘泉影老师接收研究助理、硕士和博士申请者。欢迎数学功底良好、对大脑科学感兴趣的同学,报考刘泉影老师的硕士或博士。

 

 

研究方向:

1)神经计算建模(贝叶斯模型、Drift Diffusion Model)
2)多模态成像技术(fMRI-EEG),多通道脑电(high-density EEG)源定位与脑成像
3)神经反馈控制、脑机接口(Neurofeedback,Brain-computer interface)
4)非侵入式脑刺激 (TMS, tDCS/tACS)

5)鲁棒控制与运动控制 (Robust control,Sensorimotor control)

 

教育背景
2013-2017    博士在读,瑞士苏黎世联邦理工学院,健康科学与技术学院,生物医学工程。导师:Dr. Nicole Wenderoth,Dr. Dante Mantini
2010-2013   硕士在读,中国兰州大学,信息科学与工程学院,计算机软件与理论。导师:胡斌教授
2006-2010   本科在读,中国兰州大学,信息科学与工程学院,电子信息科学与技术。

 
研究经历
2019.08-      助理教授,南方科技大学 生物医学工程学院;神经计算与控制实验室PI
2017-2019   博士后,美国加州理工学院,计算与数学科学学院 。导师: Dr. John Doyle
2017-2019   独立研究员,美国 亨廷顿医学研究中心
2016-2017   访问学者,比利时鲁汶大学,运动控制与神经可塑性实验室
2016.10        访问学者,德国弗莱堡大学,“无侵入式脑刺激”暑期培训班
2014-2015    访问学者,英国牛津大学,实验心理学院

 

研究项目
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)

 

获奖
AAIC travel award (2019);

Estes Stars Award (2018);

研究生国家奖学金(2013);  

IBM 中国优秀学生奖学金(2013);

 

 

杂志论文(包含preprint论文)
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.

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