FPGA implementation of Kalman filter for neural ensemble decoding of rat's motor cortex
FPGA implementation of Kalman filter for neural ensemble decoding of rat's motor cortex
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DOI：
10.1016/j.neucom.2011.03.044
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发表时间：
2011-10
期刊：
影响因子：
6
通讯作者：
Xiaoping Zhu;Rongxin Jiang;Yao-wu Chen;Sanqing Hu;Dong Wang-
中科院分区：
文献类型：
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作者：
Xiaoping Zhu;Rongxin Jiang;Yao-wu Chen;Sanqing Hu;Dong Wang-
高性能计算是脑机接口（BMI）应用的关键。现有的BMI译码算法多在PC机上实现，影响了复杂映射模型的译码性能。本文提出了一种新的卡尔曼滤波算法的FPGA实现方法。在水奖赏条件下，记录大鼠运动皮层神经元的整体活动。卡尔曼滤波器，这是用于映射神经活动的运动变量，在PC（基于MATLAB）和FPGA上实现。在FPGA结构中，采用基于行/列的方法代替传统的基于元素的方法进行矩阵运算，同时采用并行和流水线结构进行高效计算。结果表明，基于FPGA的实现比基于PC的实现快24.45倍，同时达到相同的精度。这种基于硬件的计算方法为高性能计算提供了一种工具，对便携式BMI应用具有深远的意义。
High performance computation is critical for brain–machine interface (BMI) applications. Current BMI decoding algorithms are always implemented on personal computers (PC) which affect the performance of complex mapping models. In this paper, an FPGA implementation of Kalman filter (KF) algorithm is proposed as a new computational method. The neural ensemble activities are recorded from motor cortex of rats performing a lever-pressing task for water reward. Kalman filter, which is used for mapping neural activities to kinematic variables, is implemented both on PC (MATLAB-based) and FPGA. In FPGA architecture, the row/column-based method is adopted for the matrix operation instead of the traditional element-based method, parallel and pipelined structures are also used for efficient computation at the same time. The results show that the FPGA-based implementation runs 24.45 times faster than the PC-based counterpart while achieving the same accuracy. Such a hardware-based computational method provides a tool for high-performance computation, with profound implications for portable BMI application.