Title of the Report: Machine Learning in Brain-Computer Interfaces

Presenter: Wu Dongrui

Affiliation: Huazhong University of Science and Technology

Date of the Report: September 29, 2024 (Sunday), 09:30-12:00 AM

Location of the Report:Meeting Room 902, Block A, Feicui Science and Education Building

Abstract:A brain-computer interface is a direct communication pathway between the human brain and external devices (such as computers and robots). Due to individual differences and the nonstationary nature of brain signals, BCIs often require personalized calibration for new users or tasks, which is time-consuming and laborious, affecting user interest. Advanced machine learning methods can help reduce or even eliminate calibration entirely, improving system accuracy and user-friendliness. This report will introduce purely data-driven methods, knowledge-data fusion methods, fuzzy system methods, and large models in the decoding of EEG signals.

Biography of the Presenter: Wu Dongrui received his Bachelor's degree in Automation from the University of Science and Technology of China in 2003, Master's degree in Electronics and Computer Engineering from the National University of Singapore in 2006, and Ph.D. in Electrical Engineering from the University of Southern California in 2009. He is a professor, Ph.D. supervisor, and assistant dean at the School of Artificial Intelligence and Automation, Huazhong University of Science and Technology. He also serves as the deputy director of the Key Laboratory of Image Information Processing and Intelligent Control under the Ministry of Education, an IEEE Fellow, the editor-in-chief of the IEEE Transactions on Fuzzy Systems (IF=10.7), and a member of the editorial board for information sciences at National Science Review. His main research interests include brain-computer interfaces and machine learning, supported by projects funded by the Ministry of Science and Technology, the National Natural Science Foundation of China, Huawei, Baidu, Alibaba, Ant Group, etc. He has published over 200 papers in journals such as Proceedings of the IEEE, IEEE TPAMI, National Science Review, with 13 highly cited papers according to ESI and over 14,000 Google Scholar citations (H-index = 62). He has been listed among the top 2% of scientists worldwide by Stanford University for six consecutive years. Two of his algorithms have been included in the MATLAB Fuzzy Logic Toolbox. Four BCI patents have been commercialized. He has received numerous awards including the First Prize in Natural Sciences from the Chinese Association of Automation (2023), named an Innovative Figure in Intelligent Computing by MIT Technology Review China (2023), the Youth Science Award from the Ministry of Education (2022), the Young Scientist Award from the Chinese Association of Automation (2021), and six Outstanding Paper Awards. From 2021 to 2022, he won the national championship in technical competitions held by the National Natural Science Foundation of China's Information Science Department, the Chinese Institute of Electronics, and Tsinghua University. In 2023, he was the national runner-up, and in 2024, he secured two grand prizes (national champions) and the first prize in three algorithm categories, with coverage by CCTV News.