Li Simin, Li Jie, Li Zheng
State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal UniversityBeijing, China; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal UniversityBeijing, China.
Front Neurosci. 2016 Dec 22;10:587. doi: 10.3389/fnins.2016.00587. eCollection 2016.
Brain-machine interfaces (BMIs) seek to connect brains with machines or computers directly, for application in areas such as prosthesis control. For this application, the accuracy of the decoding of movement intentions is crucial. We aim to improve accuracy by designing a better encoding model of primary motor cortical activity during hand movements and combining this with decoder engineering refinements, resulting in a new unscented Kalman filter based decoder, UKF2, which improves upon our previous unscented Kalman filter decoder, UKF1. The new encoding model includes novel acceleration magnitude, position-velocity interaction, and target-cursor-distance features (the decoder does not require target position as input, it is decoded). We add a novel probabilistic velocity threshold to better determine the user's intent to move. We combine these improvements with several other refinements suggested by others in the field. Data from two Rhesus monkeys indicate that the UKF2 generates offline reconstructions of hand movements (mean CC 0.851) significantly more accurately than the UKF1 (0.833) and the popular position-velocity Kalman filter (0.812). The encoding model of the UKF2 could predict the instantaneous firing rate of neurons (mean CC 0.210), given kinematic variables and past spiking, better than the encoding models of these two decoders (UKF1: 0.138, p-v Kalman: 0.098). In closed-loop experiments where each monkey controlled a computer cursor with each decoder in turn, the UKF2 facilitated faster task completion (mean 1.56 s vs. 2.05 s) and higher Fitts's Law bit rate (mean 0.738 bit/s vs. 0.584 bit/s) than the UKF1. These results suggest that the modeling and decoder engineering refinements of the UKF2 improve decoding performance. We believe they can be used to enhance other decoders as well.
脑机接口（BMI）旨在将大脑与机器或计算机直接连接起来，以应用于诸如假肢控制等领域。对于此应用，运动意图解码的准确性至关重要。我们的目标是通过设计一种更好的手部运动过程中初级运动皮层活动的编码模型，并将其与解码器工程改进相结合，从而提高准确性，由此产生了一种基于无迹卡尔曼滤波器的新解码器UKF2，它在我们之前的无迹卡尔曼滤波器解码器UKF1的基础上进行了改进。新的编码模型包括新颖的加速度大小、位置 - 速度相互作用以及目标 - 光标距离特征（解码器不需要目标位置作为输入，它是被解码出来的）。我们添加了一个新颖的概率速度阈值，以更好地确定用户的运动意图。我们将这些改进与该领域其他人提出的其他几种改进相结合。来自两只恒河猴的数据表明，UKF2生成的手部运动离线重建（平均相关系数为0.851）比UKF1（0.833）和流行的位置 - 速度卡尔曼滤波器（0.812）显著更准确。给定运动学变量和过去的脉冲发放情况，UKF2的编码模型能够比这两种解码器的编码模型（UKF1：0.138，位置 - 速度卡尔曼滤波器：0.098）更好地预测神经元的瞬时发放率（平均相关系数为0.210）。在闭环实验中，每只猴子依次使用每个解码器控制计算机光标，与UKF1相比，UKF2促进了更快的任务完成（平均1.56秒对2.05秒）和更高的费茨定律比特率（平均0.738比特/秒对0.584比特/秒）。这些结果表明，UKF2的建模和解码器工程改进提高了解码性能。我们相信它们也可用于增强其他解码器。
