Xu Kai, Wang Yiwen, Wang Fang, Liao Yuxi, Zhang Qiaosheng, Li Hongbao, Zheng Xiaoxiang
Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou 310027, China ; Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China.
Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou 310027, China ; Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou 310027, China.
Biomed Res Int. 2014;2014:685492. doi: 10.1155/2014/685492. Epub 2014 May 18.
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.
序贯蒙特卡罗方法在点过程估计上已成功应用于预测神经活动的运动。然而，该方法存在一些问题，比如调谐模型简化和计算复杂度高，这可能会降低运动脑机接口的解码性能。在本文中，我们采用一种考虑近期整体活动的通用调谐模型。拟合优度分析表明，所提出的模型比仅依赖运动学的模型能更准确地预测神经元反应。基于所提出的模型构建了一种新的序贯蒙特卡罗算法。该算法能显著降低解码结果的均方根误差，在位置估计中降低了23.6%。此外，我们通过在图形处理器（GPU）上大规模并行实现所提出的算法来加快解码速度。结果表明，即使有8000个粒子或300个神经元，尖峰序列也能作为点过程实时解码，比串行实现快10倍以上。我们工作的主要贡献是使具有点过程观测的序贯蒙特卡罗算法能更快、更准确地输出运动估计。
