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Issue No.10 - October (2008 vol.41)
pp: 34-42
Andrzej Cichocki , RIKEN Brain Science Institute
Yoshikazu Washizawa , RIKEN Brain Science Institute
Tomasz Rutkowski , RIKEN Brain Science Institute
Hovagim Bakardjian , RIKEN Brain Science Institute
Anh-Huy Phan , RIKEN Brain Science Institute
Seungjin Choi , Pohang University of Science and Technology
Hyekyoung Lee , Pohang University of Science and Technology
Qibin Zhao , Shanghai Jiao Tong University
Liqing Zhang , Shanghai Jiao Tong University
Yuanqing Li , South China University of Technology
In addition to helping better understand how the human brain works, the brain-computer interface neuroscience paradigm allows researchers to develop a new class of bioengineering control devices and robots, offering promise for rehabilitation and other medical applications as well as exploring possibilities for advanced human-computer interfaces.
brain-computer interfaces, neuroscience paradigms, Tucker model, PARAFAC model
Andrzej Cichocki, Yoshikazu Washizawa, Tomasz Rutkowski, Hovagim Bakardjian, Anh-Huy Phan, Seungjin Choi, Hyekyoung Lee, Qibin Zhao, Liqing Zhang, Yuanqing Li, "Noninvasive BCIs: Multiway Signal-Processing Array Decompositions", Computer, vol.41, no. 10, pp. 34-42, October 2008, doi:10.1109/MC.2008.431
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18. A. Cichocki, A.H. Phan, and C. Caiafa, "Flexible HALS Algorithms for Sparse Non-Negative Matrix/Tensor Factorization," Machine Learning for Signal Processing, to be published in 2008.
19. A.H. Phan and A. Cichocki, Fast and Efficient Algorithm for Nonnegative Tucker Decomposition, LNCS 5264, Springer, 2008, pp. 772–782.
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