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The impact of command signal power distribution, processing delays, and speed scaling on neurally-controlled devices

机译：命令信号功率分配，处理延迟和速度缩放对神经控制设备的影响

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摘要

Objective. Decoding algorithms for brain-machine interfacing (BMI) are typically only optimized to reduce the magnitude of decoding errors. Our goal was to systematically quantify how four characteristics of BMI command signals impact closed-loop performance: (1) error magnitude, (2) distribution of different frequency components in the decoding errors, (3) processing delays, and (4) command gain. Approach. To systematically evaluate these different command features and their interactions, we used a closed-loop BMI simulator where human subjects used their own wrist movements to command the motion of a cursor to targets on a computer screen. Random noise with three different power distributions and four different relative magnitudes was added to the ongoing cursor motion in real time to simulate imperfect decoding. These error characteristics were tested with four different visual feedback delays and two velocity gains. Main results. Participants had significantly more trouble correcting for errors with a larger proportion of low-frequency, slow-time-varying components than they did with jittery, higher-frequency errors, even when the error magnitudes were equivalent. When errors were present, a movement delay often increased the time needed to complete the movement by an order of magnitude more than the delay itself. Scaling down the overall speed of the velocity command can actually speed up target acquisition time when low-frequency errors and delays are present. Significance. This study is the first to systematically evaluate how the combination of these four key command signal features (including the relatively-unexplored error power distribution) and their interactions impact closed-loop performance independent of any specific decoding method. The equations we derive relating closed-loop movement performance to these command characteristics can provide guidance on how best to balance these different factors when designing BMI systems. The equations reported here also provide an efficient way to compare a diverse range of decoding options offline.
机译：目的。通常仅针对脑机接口（BMI）的解码算法进行优化，以减少解码错误的幅度。我们的目标是系统地量化BMI命令信号的四个特征如何影响闭环性能：（1）误差幅度，（2）解码错误中不同频率分量的分布，（3）处理延迟和（4）命令增益。方法。为了系统地评估这些不同的命令功能及其相互作用，我们使用了一个闭环BMI模拟器，在该模拟器中，人类受试者使用自己的手腕运动来命令将光标移动到计算机屏幕上的目标。将具有三种不同功率分布和四种不同相对幅度的随机噪声实时添加到正在进行的光标运动中，以模拟不完美的解码。这些误差特性在四个不同的视觉反馈延迟和两个速度增益下进行了测试。主要结果。与低频抖动时频变化较大的参与者相比，即使在误差幅度相等的情况下，参与者在纠正具有较大比例的低频，时变变化分量的错误时也要面对更多的麻烦。当出现错误时，运动延迟通常会使完成运动所需的时间比延迟本身增加一个数量级。当存在低频误差和延迟时，降低速度命令的整体速度实际上可以加快目标获取时间。意义。这项研究是第一个系统地评估这四个关键命令信号特征（包括相对未开发的误差功率分布）及其相互作用如何与闭环性能无关的，与任何特定解码方法无关的组合的方法。我们得出的将闭环运动性能与这些命令特征相关联的方程式可为设计BMI系统时如何最佳地平衡这些不同因素提供指导。此处报告的公式还提供了一种有效的方法，可以离线比较各种解码选项。

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  • 来源
    《Journal of neural engineering》 |2015年第4期|046031.1-046031.12|共12页
  • 作者

    A R Marathe; D M Taylor;

  • 作者单位

    Department of Neurosciences, The Cleveland Clinic, Cleveland, OH 44195, USA,Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA,Cleveland Functional Electrical Stimulation (FES) Center of Excellence, Louis Stokes VA Medical Center, Cleveland, OH 44106, USA,Human Research and Engineering Directorate, US Army Research Laboratory, Aberdeen Proving Ground, MD 21005, USA;

    Department of Neurosciences, The Cleveland Clinic, Cleveland, OH 44195, USA,Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA,Cleveland Functional Electrical Stimulation (FES) Center of Excellence, Louis Stokes VA Medical Center, Cleveland, OH 44106, USA;

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  • 正文语种 eng
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  • 关键词

    brain-machine interface; brain-computer interface; closed-loop control; neuroprosthesis; visually-guided reaching;

    机译：脑机接口;脑机接口;闭环控制;神经假体视觉引导的到达;

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