Hsieh Han-Lin, Shanechi Maryam M
Annu Int Conf IEEE Eng Med Biol Soc. 2015 Aug;2015:1667-70. doi: 10.1109/EMBC.2015.7318696.
Closed-loop decoder adaptation (CLDA) can improve brain-machine interface (BMI) performance. CLDA methods use batches of data to refit the decoder parameters in closed-loop operation. Recently, dynamic state-space algorithms have also been designed to fit the parameters of a point process decoder (PPF). A main design parameter that needs to be selected in any CLDA algorithm is the learning rate, i.e., how fast should the decoder parameters be updated on the basis of new neural observations. So far, the learning rate of CLDA algorithms has been selected empirically using ad-hoc methods. Here we develop a principled framework to calibrate the learning rate in adaptive state-space algorithms. The learning rate introduces a trade-off between the convergence rate and the steady-state error covariance of the estimated decoder parameters. Hence our algorithm first finds an analytical upper-bound on the steady-state error covariance as a function of the learning rate. It then finds the inverse mapping to select the optimal learning rate based on the maximum allowable steady-state error. Using numerical BMI experiments, we show that the calibration algorithm selects the optimal learning rate that meets the requirement on steady-state error level while achieving the fastest convergence rate possible corresponding to this steady-state level.
闭环解码器自适应（CLDA）可以提高脑机接口（BMI）的性能。CLDA方法在闭环操作中使用一批数据来重新拟合解码器参数。最近，动态状态空间算法也被设计用于拟合点过程解码器（PPF）的参数。在任何CLDA算法中都需要选择的一个主要设计参数是学习率，即根据新的神经观测，解码器参数应以多快的速度更新。到目前为止，CLDA算法的学习率一直是通过临时方法凭经验选择的。在这里，我们开发了一个有原则的框架来校准自适应状态空间算法中的学习率。学习率在估计的解码器参数的收敛速度和稳态误差协方差之间引入了一种权衡。因此，我们的算法首先找到作为学习率函数的稳态误差协方差的解析上界。然后找到逆映射，以根据最大允许稳态误差选择最优学习率。通过数值BMI实验，我们表明校准算法选择的最优学习率满足稳态误差水平的要求，同时实现了对应于该稳态水平的最快收敛速度。
