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
Abstract: 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.
