Yiwen Wang

Yiwen Wang (S’05–M’08) received the B.S. and M.S. degrees from University of Science and Technology of China, in 2001 and 2004, respectively. She received the Ph.D. degree from University of Florida, Gainesville, FL, USA, in 2008. She then joined the Department of Electronics and Computer Engineering as a Research Associate at the Hong Kong University of Science and Technology, Kowloon, Hong Kong. In 2010, she joined the faculty of Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China. She is currently an Associate Professor there. Her research interests include neural decoding of brain-machine interfaces, adaptive signal processing, computational neuroscience, and neuromorphic engineering.

Associated articles

TBME, Featured Articles
Tracking Neural Modulation Depth by Dual Sequential Monte Carlo Estimation on Point Processes for Brain Machine Interfaces
Yiwen Wang, Xiwei She, Yuxi Liao, Hongbao Li, Qiaosheng Zhang, Shaomin Zhang, Xiaoxiang Zheng and Jose Principe. Zhejiang University and University of Florida Classic Brain Machine Interface (BMI) approaches decode neural signals from the brain responsible for achieving specific motor movements,... Read more
TNSRE, Featured Articles
Neural Control of a Tracking Task via Attention-Gated Reinforcement Learning for Brain-Machine Interfaces
Abstract Reinforcement learning (RL)-based brain machine interfaces (BMIs) enable the user to learn from the environment through interactions to complete the task without desired signals, which is promising for clinical applications. Previous studies exploited Q-learning techniques to discriminate neural states into simple directional actions providing the... Read more
TNSRE, Featured Articles
Clustering Neural Patterns in Kernel Reinforcement Learning Assists Fast Brain Control in Brain-Machine Interfaces
     Neuroprosthesis enables the brain control on the external devices purely using neural activity for paralyzed people. Supervised learning decoders recalibrate or re-fit the discrepancy between the desired target and decoder’s output, where the correction may over-dominate the user’s intention. Reinforcement... Read more