摘要基于神经元放电信号的脑-机接口(BCI)的核心问题是获得神经元锋电位的放电率，然后利用神经元群体解码算法解码运动轨迹。综述了当前及经典的放电率估计方法的理论依据，并对各种算法的优缺点进行了比较；同时介绍了BCI中利用放电率解码运动轨迹的主要算法：群矢量算法、线性滤波及卡尔曼滤波；最后介绍了将放电率估计算法应用于BCI中解码实际手臂运动的结果。结果显示：不同算法产生的放电率估计有所不同，但实际应用时的解码效果差别不明显。

abstractsThe core problem of the brain-computer interface (BCI) based on neural signal is estimating neural firing rate from a spike train and then using neural population decoding algorithm to decode movement trajectory.In this artical, we review the theoretical basis of both classic and current firing rate estimations and compare the advantages and drawbacks of these methods. At the same time we also review the decoding algorithm which using neural firing rate to decode movement trajectory in brain- computer interface: population vector algorithm, linear filter and kalman filter. At last, some results applying these estimators of firing rate to decode arm movement in BCI are introduced. The results show apparently different performance of the different firing rate estimators, while minimal differences are observed in the actual application of BCI.