Brain-state classification and a dual-state decoder dramatically improve the control of cursor movement through a brain-machine interface.
Brain-state classification and a dual-state decoder dramatically improve the control of cursor movement through a brain-machine interface.
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DOI：
10.1088/1741-2560/13/1/016009
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发表时间：
2016-02
影响因子：
4
通讯作者：
Miller LE
中科院分区：
文献类型：
--
作者：
Sachs NA;Ruiz-Torres R;Perreault EJ;Miller LE
值得注意的是，脑机接口（BMIs）可以用不到100个神经元来控制复杂的运动。成功的部分原因可能是由于大多数BMI测试的动态条件范围有限。通过单一线性映射实现跨越这些条件的高质量控制将更具挑战性。即使对于简单的到达运动，现有的BMI也必须通过随时间平均控制信号来减少神经元的随机噪声，而不是在通常控制运动的许多神经元上。这迫使在具有允许快速移动的动态的解码器和允许以小抖动保持姿势的解码器之间进行折衷。我们目前的工作提出了一种解决这种妥协的方法，它也可以推广到更高度变化的动态情况，包括速度变化更大的运动。我们已经开发出一个系统，使用两个独立的韦纳过滤器作为单独的组件在一个解码器，一个优化的运动，和其他姿势控制。我们使用相同的神经输入计算LDA分类器。分类器将两个滤波器的输出与分类器分配给每个状态的可能性成比例地组合。我们用两只猴子进行了在线实验，使用这种神经分类器，双状态解码器，将其与标准的单状态解码器以及根据光标与目标的接近度自动切换状态的双状态解码器进行比较。使用分类器解码器的两只猴子的性能明显优于单状态解码器，并且与邻近解码器相当。我们已经展示了一种新的策略，用于处理快速移动的需求，同时在接近和稳定目标时保持精确的光标控制。通过优化各个运动和姿态解码器的性能，无疑可以实现进一步的增益。
It is quite remarkable that Brain Machine Interfaces (BMIs) can be used to control complex movements with fewer than 100 neurons. Success may be due in part to the limited range of dynamical conditions under which most BMIs are tested. Achieving high-quality control that spans these conditions with a single linear mapping will be more challenging. Even for simple reaching movements, existing BMIs must reduce the stochastic noise of neurons by averaging the control signals over time, instead of over the many neurons that normally control movement. This forces a compromise between a decoder with dynamics allowing rapid movement and one that allows postures to be maintained with little jitter. Our current work presents a method for addressing this compromise, which may also generalize to more highly varied dynamical situations, including movements with more greatly varying speed. We have developed a system that uses two independent Weiner filters as individual components in a single decoder, one optimized for movement, and the other for postural control. We computed an LDA classifier using the same neural inputs. The classifier combined the outputs of the two filters in proportion to the likelihood assigned by the classifier to each state. We have performed online experiments with two monkeys using this neural-classifier, dual-state decoder, comparing it to a standard, single-state decoder as well as to a dual-state decoder that switched states automatically based on the cursor’s proximity to a target. The performance of both monkeys using the classifier decoder was markedly better than that of the single-state decoder and comparable to the proximity decoder. We have demonstrated a novel strategy for dealing with the need to make rapid movements while also maintaining precise cursor control when approaching and stabilizing within targets. Further gains can undoubtedly be realized by optimizing the performance of the individual movement and posture decoders.
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影响因子：
64.8
作者：
Ethier, C.;Oby, E. R.;Bauman, M. J.;Miller, L. E.
通讯作者：
Miller, L. E.
影响因子：
2.5
作者：
Golub, Matthew D.;Yu, Byron M.;Chase, Steven M.
通讯作者：
Chase, Steven M.
影响因子：
25
作者：
Kurtzer, I;Herter, TM;Scott, SH
通讯作者：
Scott, SH
影响因子：
9.8
作者：
Ganguly K;Carmena JM
通讯作者：
Carmena JM
对于四肢瘫痪患者，在心脏内脑机构界面中闭环校准的优点。
影响因子：
4
作者：
Jarosiewicz B;Masse NY;Bacher D;Cash SS;Eskandar E;Friehs G;Donoghue JP;Hochberg LR
通讯作者：
Hochberg LR