The SSVEP-based BCI technology proposed by Professor Gao features high transmission rate and a large number of recognizable targets, becoming one of the main paradigms in the BCI field
Has published more than 200 academic papers, with over 20,000 citations on Google Scholar
Listed in Elsevier's China Highly Cited Researchers for more than a decade consecutively
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
He has 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. Yike Sun, Yaxuan Gao, Kewei Wang, Jingnan Sun, Yuzhen Chen, Changxing Huang, Xiaoyang Li, Yanan Yang, Tianhua Zhao, Haochen Zhu, Ran Liu, Xiaogang Chen, Bai Lu, Xiaorong Gao, Lateral ventricular brain-computer interface system with lantern-inspired electrode for stable performance and memory-guided decoding, National Science Review, 2026, nwag081. (IF=17.1)
2. Xiaorong Gao, Yijun Wang, Xiaogang Chen, Bingchuan Liu, Shangkai Gao, Brain–computer interface—a brain-in-the-loop communication system, Proceedings of the IEEE, 2025, 113(5): 478-511. (IF=25.9)
3. Yonghao Song, Yijun Wang, Huiguang He, Xiaorong Gao, Recognizing natural images from EEG with language-guided contrastive learning, IEEE Transactions on Neural Networks and Learning Systems, 2025, 36(9): 15896-15910. (IF=8.9)
4. Bo Dai, Yijun Wang, Xinyu Mou, Xiaorong Gao, A reliability-enhanced brain–computer interface via mixture-of-graphs-driven information fusion, Information Fusion, 2025, 120: 103069. (IF=15.5)
5. Yike Sun, Yuhan Li, Yuzhen Chen, Chen Yang, Jingnan Sun, Liyan Liang, Xiaogang Chen, Xiaorong Gao, Efficient dual-frequency SSVEP brain-computer interface system exploiting interocular visual resource disparities, Expert Systems with Applications, 2024, 252: 124144. (IF=7.5)
6. Zhouheng Wang, Nanlin Shi, Yingchao Zhang, Ning Zheng, Haicheng Li, Yang Jiao, Jiahui Cheng, Yutong Wang, Xiaoqing Zhang, Ying Chen, Yihao Chen, Heling Wang, Tao Xie, Yijun Wang, Yinji Ma, Xiaorong Gao, Xue Feng, Conformal in-ear bioelectronics for visual and auditory brain-computer interfaces, Nature Communications, 2023, 14: 4213. (IF=14.7)
7. Xiang Li, Jingjing Chen, Nanlin Shi, Chen Yang, Puze Gao, Xiaogang Chen, Yijun Wang, Shangkai Gao, Xiaorong Gao, A hybrid steady-state visual evoked response-based brain-computer interface with MEG and EEG, Expert Systems with Applications, 2023, 223: 119736. (IF=7.5)
8. Yonghao Song, Qingqing Zheng, Bingchuan Liu, Xiaorong Gao, EEG conformer: Convolutional transformer for EEG decoding and visualization, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022, 31: 710-719. (IF=4.9)
9. Xiaorong Gao, Yijun Wang, Xiaogang Chen, Shangkai Gao, Interface, interaction, and intelligence in generalized brain–computer interfaces, Trends in Cognitive Sciences, 2021, 25(8): 671-684. (IF=24.482)
10. Xiaogang Chen, Yijun Wang, Masaki Nakanishi, Xiaorong Gao, Tzyy-Ping Jung, Shangkai Gao, High-speed spelling with a noninvasive brain-computer interface, Proceedings of the National Academy of Sciences of the United States of America, 2015, 122(44): E6058-E6067. (IF=9.423)
