Assoc. Prof. Rui Yang
Xi'an Jiaotong Liverpool University, China

Rui Yang received the B.Eng. degree in Computer Engineering and the Ph.D. degree in Electrical and Computer Engineering from National University of Singapore in 2008 and 2013 respectively. He is currently an Associate Professor in the School of Advanced Technology, Xi’an Jiaotong-Liverpool University, Suzhou, China, and an Honorary Lecturer in the Department of Computer Science, University of Liverpool, Liverpool, United Kingdom. His research interests include machine learning based data analysis and applications. Dr. Yang is currently serving as an Associate Editor for Neurocomputing, and a very active reviewer for many international journals and conferences. He is supervising 13 PhD students and has published more than 80 research papers.

Speech Title: Single-Source to Single-Target Cross-Subject Motor Imagery Classification Based on Multi-Subdomain Adaptation Network
In the electroencephalography based cross-subject motor imagery (MI) classification task, the device and subject problems can cause the time-related data distribution shift problem. In a single-source to single-target (STS) MI classification task, such a shift problem will certainly provoke an increase in the overall data distribution difference between the source and target domains, giving rise to poor classification accuracy. Therefore, we propose a novel multi-subdomain adaptation method (MSDAN) to solve the shift problem and improve the classification accuracy of the traditional approaches. In the proposed MSDAN, the adaptation losses in both class-related and time-related subdomains (that are divided by different data labels and session labels) are obtained by measuring the distribution differences between the source and target subdomains. Then, the adaptation and classification losses in the loss function of MSDAN are minimized concurrently. To illustrate the application value of the proposed method, our method is applied to solve the STS MI classification task about data analysis with respect to the brain-computer interface competition III-IVa dataset. The resultant experiment results demonstrate that compared with other well-known domain adaptation and deep learning methods, the proposed method is capable of solving the time-related data distribution problem at higher classification accuracy.

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