Bias, optimal linear estimation, and the differences between open-loop simulation and closed-loop performance of spiking-based brain-computer interface algorithms.
Bias, optimal linear estimation, and the differences between open-loop simulation and closed-loop performance of spiking-based brain-computer interface algorithms.
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偏差，最佳线性估计以及基于峰值的大脑计算机界面算法的开环模拟与闭环性能之间的差异。
DOI：
10.1016/j.neunet.2009.05.005
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发表时间：
2009-11
期刊：
影响因子：
--
通讯作者：
Kass RE
中科院分区：
文献类型：
--
作者：
Chase SM;Schwartz AB;Kass RE
数十个同时记录的神经元的活动可以用来控制机械臂或计算机屏幕上光标的移动。这种运动神经假体技术激发了人们对可以推断运动意图的算法的越来越大的兴趣。这些算法中最简单的是种群向量算法(PVA)，其中每个细胞的活动被用来加权指向该神经元首选方向的一个向量。离线后，可以证明更复杂的算法，如最优线性估计器(OLE)，可以在PVA上显著提高重建手运动的精度。我们称之为开环性能。相比之下，这种性能差异在闭环在线控制中可能不存在。开环控制和闭环控制之间的明显区别是能够适应当时使用的解码器的具体情况。为了预测算法在闭环控制中可能产生的性能收益，有必要建立一个模型来捕获这种适应过程的各个方面。在这里，我们提出了一个框架，用于对PVA和OLE的闭环性能进行建模。通过模拟和实验，我们表明：(1)某些解码器的性能增益可能远低于离线结果预测的极限；(2)受试者能够补偿解码器中的某些类型的偏差；(3)必须注意确保估计误差不会降低理论上最优解码器的性能。
The activity of dozens of simultaneously recorded neurons can be used to control the movement of a robotic arm or a cursor on a computer screen. This motor neural prosthetic technology has spurred an increased interest in the algorithms by which motor intention can be inferred. The simplest of these algorithms is the population vector algorithm (PVA), where the activity of each cell is used to weight a vector pointing in that neuron’s preferred direction. Off-line, it is possible to show that more complicated algorithms, such as the optimal linear estimator (OLE), can yield substantial improvements in the accuracy of reconstructed hand movements over the PVA. We call this open-loop performance. In contrast, this performance difference may not be present in closed-loop, on-line control. The obvious difference between open and closed-loop control is the ability to adapt to the specifics of the decoder in use at the time. In order to predict performance gains that an algorithm may yield in closed-loop control, it is necessary to build a model that captures aspects of this adaptation process. Here we present a framework for modeling the closed-loop performance of the PVA and the OLE. Using both simulations and experiments, we show that (1) the performance gain with certain decoders can be far less extreme than predicted by off-line results, (2) that subjects are able to compensate for certain types of bias in decoders, and (3) that care must be taken to ensure that estimation error does not degrade the performance of theoretically optimal decoders.
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