Yuanqing Li

Summary

Affiliation: RIKEN Brain Science Institute
Country: Japan

Publications

  1. ncbi request reprint Analysis of sparse representation and blind source separation
    Yuanqing Li
    Laboratory for Advanced Brain Signal Processing and RIKEN Brain Science Institute, Wako Shi, Saitama, 3510198, Japan
    Neural Comput 16:1193-234. 2004
  2. ncbi request reprint A network model for blind source extraction in various ill-conditioned cases
    Yuanqing Li
    Institute of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China
    Neural Netw 18:1348-56. 2005
  3. ncbi request reprint Blind estimation of channel parameters and source components for EEG signals: a sparse factorization approach
    Yuanqing Li
    Institute of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China
    IEEE Trans Neural Netw 17:419-31. 2006
  4. ncbi request reprint An extended EM algorithm for joint feature extraction and classification in brain-computer interfaces
    Yuanqing Li
    Neural Comput 18:2730-61. 2006
  5. ncbi request reprint A semi-supervised SVM learning algorithm for joint feature extraction and classification in brain computer interfaces
    Yuanqing Li
    Inst for Infocomm Res, Singapore
    Conf Proc IEEE Eng Med Biol Soc 1:2570-3. 2006
  6. ncbi request reprint An effective BCI speller based on semi-supervised learning
    Huiqi Li
    Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore
    Conf Proc IEEE Eng Med Biol Soc 1:1161-4. 2006

Detail Information

Publications6

  1. ncbi request reprint Analysis of sparse representation and blind source separation
    Yuanqing Li
    Laboratory for Advanced Brain Signal Processing and RIKEN Brain Science Institute, Wako Shi, Saitama, 3510198, Japan
    Neural Comput 16:1193-234. 2004
    ..It is also robust to additive noise and estimation error in the mixing matrix. Finally, four simulation examples and an EEG data analysis example are presented to illustrate the algorithm's utility and demonstrate its performance...
  2. ncbi request reprint A network model for blind source extraction in various ill-conditioned cases
    Yuanqing Li
    Institute of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China
    Neural Netw 18:1348-56. 2005
    ..Simulation results are also presented to show the validity of the theoretical results and the performance and characteristics of the learning algorithm...
  3. ncbi request reprint Blind estimation of channel parameters and source components for EEG signals: a sparse factorization approach
    Yuanqing Li
    Institute of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China
    IEEE Trans Neural Netw 17:419-31. 2006
    ..Several interesting findings were obtained, especially that memory-related synchronization and desynchronization appear in the alpha band, and that the strength of alpha band synchronization is related to memory performance...
  4. ncbi request reprint An extended EM algorithm for joint feature extraction and classification in brain-computer interfaces
    Yuanqing Li
    Neural Comput 18:2730-61. 2006
    ..The convergence of the algorithm and robustness of CSP feature are also demonstrated in our data analysis...
  5. ncbi request reprint A semi-supervised SVM learning algorithm for joint feature extraction and classification in brain computer interfaces
    Yuanqing Li
    Inst for Infocomm Res, Singapore
    Conf Proc IEEE Eng Med Biol Soc 1:2570-3. 2006
    ..We applied our method to data from BCI competition 2005, and the results demonstrated the validity of the proposed algorithm...
  6. ncbi request reprint An effective BCI speller based on semi-supervised learning
    Huiqi Li
    Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore
    Conf Proc IEEE Eng Med Biol Soc 1:1161-4. 2006
    ..This semi-supervised learning approach is applied on-line to obtain robust and adaptive model for P300 based speller with small training set, which is believed to be very essential to improve the feasibility of the P300 based BCI...