Gao, Xiaorong

Date:2018-11-08
Professor, School of Medicine, Tsinghua University
PI, IDG/McGovern Institute, Tsinghua University

Office: B205, Medicial Sciences Building
Phone: +86-10-62781539
Email: gxr-dea@tsinghua.edu.cn
Lab homepage: http://neuro.med.tsinghua.edu.cn

[Research Focus]

Professor Gao's research interests lie in two sub-branches of neural engineering: neural signal processing and brain computer interfaces (BCI). These two sub-branches of neural engineering use computational techniques to explore the inner workings of the brain through distinct perspectives.

The BCI study covers three aspects of human brain exploration: understanding the brain, protecting the brain, and creating the brain. The BCI technology has become a hot research topic in the areas of neuroscience, neural engineering, medicine, and rehabilitation.

Professor Gao’s long-term research goals are to create effective tools for the theoretical and practical analysis of EEG data, with broad applications ranging from basic neurophysiology studies through clinical studies to neural engineering that seek to shed light on how brain functions and why they operates in particular ways.

[Education & Experience]
2013-Present   PI, IDG/McGovern Institute, Tsinghua University
2004-Present   Professor, Dept. of Biomedical Engineering, Tsinghua University
1995-2004       Associate Professor, Dept. of Biomedical Engineering, Tsinghua University
1992-1995       Assistant Professor, Dept. of Electrical Engineering, Tsinghua University
1989-1992       Ph.D. in Biomedical Engineering, Tsinghua University
1986-1989       M.S. in Biomedical Engineering, Peking Union Medicine University, China
1981-1986       B.A. in Biomedical Engineering, Zhejiang University, China

[Selected Publications]

  • Chen X, Wang Y, Zhang S, Gao S, Hu Y, Gao X*, A novel stimulation method for multi-class SSVEP-BCI using intermodulation frequencies. J. Neural Eng., 14(2): 026013, 2017.
  • Wu C, Lin K, Wu W, Gao X*, A novel algorithm for learning sparse spatio-spectral patterns for event-related potentials. IEEE Trans. Neural Netw. Learn. Syst., 28(4): 862-872, 2016.
  • Lin K, Cinetto A, Wang Y, Chen X, Gao S, Gao X*, An online hybrid BCI system based on SSVEP and EMG. J. Neural Eng., 13(2): 026020, 2016.
  • Lin Y, Liu B, Liu Z, Gao X*, EEG gamma-band activity during audiovisual speech comprehension in different noise environments. Cogn. Neurodyn., 9(4): 389-398, 2015.
  • Chen X, Wang Y, Gao S, Jung T P, Gao X*, Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain-computer interface. J. Neural Eng., 12(4): 046008, 2015.
  • Ying J, Zhou D, Lin K, Gao X, Network analysis of functional brain connectivity driven by gamma-band auditory steady-state response in auditory hallucinations. J. Med. Biol. Eng., 35(1): 45-51, 2015.
  • Wu W, Wu C, Gao S, Liu B, Li Y, Gao X*, Bayesian estimation of ERP components from multicondition and multichannel EEG. Neuroimage. 88: 319-339, 2014.
  • Lin Y, Wu W, Wu C, Liu B, Gao X*, Extraction of mismatch negativity using a resampling-based spatial filtering method. J. Neural Eng., 10(2): 026015, 2013.
  • Guangyu Bin, Xiaorong Gao*, Yijun Wang, Bo Hong, and Shangkai Gao, A high-speed BCI based on code modulation VEP,Journal of Neural Engineering. 8 (2011) 025015 (5pp) doi:10.1088/1741-2560/8/2/025015.
  • Guangyu Bin, Xiaorong Gao*, Wang Y., Hong B. and Gao S.. VEP Based Brain-Computer Interfaces: Time,Frequency and Code Modulations. IEEE Computational Intelligence Magazine, 4: 22–26, 2009.
  • M Cheng; Xiaorong Gao; S Gao*; D Xu, Design and implementation of a brain-computer interface with high transfer rates, IEEE Transactions on Biomedical Engineering, Vol.49 No.10, p1181 – 1186, 2002.