Optimal input selection for neural machine interfaces predicting multiple non-explicit outputs.
Optimal input selection for neural machine interfaces predicting multiple non-explicit outputs.
复制标题
预测多个非显式输出的神经机器接口的最佳输入选择。
DOI：
10.1109/iembs.2008.4649327
复制
发表时间：
2008
期刊：
影响因子：
--
通讯作者：
Perreault,EricJ
中科院分区：
文献类型：
--
作者：
Krepkovich,EileenT;Perreault,EricJ
本研究实现了一种新的算法，该算法最佳地选择了用于控制多个输出的神经机器接口（NMI）设备的输入，并评估了其在缺乏显式输出的系统上的性能。NMI通常包含来自多个生理源的信号，并为多维控制提供预测，从而导致多输入多输出系统。此外，NMI通常用于患有运动障碍的受试者，因此缺乏明确的运动输出。我们的算法进行了测试，模拟多输入多输出系统和肌电图和运动学数据收集的健康受试者进行手臂达到。在模拟系统中的输出噪声的影响表明，该算法可能是有用的系统与穷人的输出状态估计，是真实的系统缺乏明确的电机输出。为了测试对生理数据的功效，使用来自一个受试者的输入和来自不同受试者的输出进行选择。选择对于这些情况是有效的，再次表明该算法对于没有运动输出的预测是有用的，因为通常是残疾受试者的情况。此外，预测结果针对不用于估计的不同运动类型进行了概括。这些结果表明，该算法的神经机接口的发展的有效性。
This study implemented a novel algorithm that optimally selects inputs for neural machine interface (NMI) devices intended to control multiple outputs and evaluated its performance on systems lacking explicit output. NMIs often incorporate signals from multiple physiological sources and provide predictions for multidimensional control, leading to multiple-input multiple-output systems. Further, NMIs often are used with subjects who have motor disabilities and thus lack explicit motor outputs. Our algorithm was tested on simulated multiple-input multiple-output systems and on electromyogram and kinematic data collected from healthy subjects performing arm reaches. Effects of output noise in simulated systems indicated that the algorithm could be useful for systems with poor estimates of the output states, as is true for systems lacking explicit motor output. To test efficacy on physiological data, selection was performed using inputs from one subject and outputs from a different subject. Selection was effective for these cases, again indicating that this algorithm will be useful for predictions where there is no motor output, as often is the case for disabled subjects. Further, prediction results generalized for different movement types not used for estimation. These results demonstrate the efficacy of this algorithm for the development of neural machine interfaces.