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BioMed Research International - Volume 2014 2014, Article ID 685492, 11 pages -

Research Article

Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou 310027, China

Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China

Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou 310027, China

Received 28 February 2014; Accepted 17 April 2014; Published 18 May 2014

Academic Editor: Ting Zhao

Copyright © 2014 Kai Xu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Sequential Monte Carlo estimation on point processes has been successfully applied to predict the movement from neural activity. However, there exist some issues along with this method such as the simplified tuning model and the high computational complexity, which may degenerate the decoding performance of motor brain machine interfaces. In this paper, we adopt a general tuning model which takes recent ensemble activity into account. The goodness-of-fit analysis demonstrates that the proposed model can predict the neuronal response more accurately than the one only depending on kinematics. A new sequential Monte Carlo algorithm based on the proposed model is constructed. The algorithm can significantly reduce the root mean square error of decoding results, which decreases 23.6% in position estimation. In addition, we accelerate the decoding speed by implementing the proposed algorithm in a massive parallel manner on GPU. The results demonstrate that the spike trains can be decoded as point process in real time even with 8000 particles or 300 neurons, which is over 10 times faster than the serial implementation. The main contribution of our work is to enable the sequential Monte Carlo algorithm with point process observation to output the movement estimation much faster and more accurately.

Autor: Kai Xu, Yiwen Wang, Fang Wang, Yuxi Liao, Qiaosheng Zhang, Hongbao Li, and Xiaoxiang Zheng



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