Keynote Speakers

Dongrui Wu
Huazhong University of Science and Technology, China

Dongrui Wu (IEEE Fellow) received a B.E in Automatic Control from the University of Science and Technology of China, Hefei, China, in 2003, an M.Eng in Electrical and Computer Engineering from the National University of Singapore in 2006, and a PhD in Electrical Engineering from the University of Southern California, Los Angeles, CA, in 2009. He is now Professor and Deputy Director of the Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China.

Prof. Wu's research interests include brain-computer interface, machine learning, computational intelligence, and affective computing. He has more than 200 publications (13000+ Google Scholar citations; h=60). He received the IEEE Computational Intelligence Society  Outstanding PhD Dissertation Award in 2012, the IEEE Transactions on Fuzzy Systems Outstanding Paper Award in 2014, the IEEE Systems, Man and Cybernetics Society Early Career Award in 2017, the USERN Prize in Formal Sciences in 2020, the IEEE Transactions on Neural Systems and Rehabilitation Engineering Best Paper Award in 2021, the Chinese Association of Automation (CAA) Early Career Award in 2021, the Ministry of Education Young Scientist Award in 2022, and First Prize of the CAA Natural Science Award in 2023. His team won National Champion of the China Brain-Computer Interface Competition in two successive years (2021-2022). Prof. Wu is the Editor-in-Chief of IEEE Transactions on Fuzzy Systems.

Speech Title: Knowledge-Data Fusion based EEG Signal Decoding

Abstract: A brain-computer interface (BCI) is a direct communication pathway between the brain and an external device, which can be used to research, enhance or repair human cognitive and sensory-motor functions. Accurate decoding of brain signals is fundamental to the applications of BCIs. Most existing BCI decoding algorithms are pure data-driven, requiring lots of calibration data. This talk introduces knowledge-data fusion based EEG signal decoding approaches, which can effectively reduce the calibration data requirement.