Gao Xiaorong-School of Medicine, Tsinghua University

Neural Engineering

Gao Xiaorong

Professor, School of medicine, Tsinghua University

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E-mail: gxr-dea@tsinghua.edu.cn
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Engaged in brain computer interface research for 20 years, proposed and implemented brain computer interface technology based on steady-state evoked potential

Personal homepage:http://www.ncbi.nlm.nih.gov/pubmed/?term=Xiaorong+Gao+Tsinghua Professor Gao Xiaorong received his bachelor's degree from Zhejiang University in 1986.He obtained a master of medicine from Peking Union Medical College in 1989.He received a doctorate from Tsinghua University in 1992 and then taught at Tsinghua University. He has successively served as a lecturer, associate professor, professor and long-term professor. In 2004, he was selected as the "academic Newcomer Award" of Tsinghua University. He achieved 2019 China cardiology outstanding contribution award. He has been Chairman designate of medical neuro-engineering branch of Chinese society of biomedical engineering.

Engaged in the research on brain computer interface and cognitive function increase, the core technologyofbrain computer interface based on steady-state visual evoked potential,and cognitive enhancement technology based on EEG neural feedback

Professor Gao Xiaorong's key research fields and main research achievements include:

·Development of high speed brain computer interface system

Hehas maintained the world leading level in the field of high-speed brain computer interface for a long time, and has developed a series of visual brain computer interface systems with the world's highest communication rate.The researchof"brain computer interface high-speed text spelling" published in the proceedings of the National Academy of Sciences in 2015 has achieved the world's highest communication rate of 267 bits / min.

·Development of high performance EEG signal acquisition equipment

Relying on the research strength of the laboratory, the 64 channel high-performance EEG signal acquisition equipment was successfully developed and successfully applied in the first China brain computer interface competition in 2010.At present, many laboratories in China use this equipment to carry out the research of EEG and brain computer interface.

·Algorithm of EEG signal analysis and processing

The developed method based on EEG time-space-frequency domain feature joint analysis has won the global brain computer interface data competition for many times, showing its excellent performance.It has won the first place in five individual events in several global "brain computer interface data competitions", and the results have obvious advantages compared with other research institutions.This paper studies the changes of EEG activities related to alertness and sleepiness, and puts forward the application of EEG in alertness monitoring, EEG pattern classification based on motor consciousness, and EEG activity classification related to emotion.

1. Xiaogang Chen, Yijun Wang, Masaki Nakanishi, * Xiaorong Gao, TZYY Ping Jung, SHANGKAI Gao, high speed spelling with a noninvasive brain computer interface, procedures of the National Academy of Sciences of the United States of America, 122 (44), 2015 / 10 / 1, SCI, Journal Papers (CAS classification zone I, if = 9.661)

2. A study on reducing training time of BCI system based on an SSVEP dynamic model，Han, Xu; Zhang, Shangen; Gao, Xiaorong, 7th International Winter Conference on brain computer interface, BCI, 2019

3. An online brain-computer interface in mobile virtual reality environments，Yao, Zhaolin; Wang, Yijun; Yang, Chen; Pei, Weihua; Gao, Xiaorong; Chen, Hongda, integrated Computer-Aided Engineering (impact factor 4.904), 2019, Volume 26, issue 4, page 345-360, wos accession No.: 000486683200003

4. A novel system of SSVEP-based human-robot coordination，Han, Xu; Lin, Ke; Gao, Shangkai; Gao, Xiaorong, Journal of National Engineering (impact factor 4.551), 2019, Volume 16, issue 1, wos accession No.: 000450311900006

5. Combination of high-frequency SSVEP-based BCI and computer vision for controlling a robotic arm，Chen, Xiaogang; Zhao, Bing; Wang, Yijun; Gao, Xiaorong, Journal of National Engineering (impact factor 4.551), 2019, Volume 16, issue 2, wos accession No.: 000457188300004

6. The effect of visual stimuli noise and fatigue on steady-state visual evoked potentials，Zhang, Shangen; Gao, Xiaorong, Journal of National Engineering (impact factor 4.551), 2019, Volume 16, issue 5, wos accession No.: 000485727600001

7. Xu Chaoli, Lin Ke, Yang Chen, Wu Chaohua, Gao Xiaorong. Intelligent robot cooperative control method based on calf surface electromyography [J]. Chinese Journal of Biomedical Engineering, 2016, 35 (4), 385-393. Chinese article