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Feedback control policies employed by people using intracortical brain-computer interfaces

机译：人们使用皮质内脑机接口使用的反馈控制策略

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摘要

Objective. When using an intracortical BCI (iBCI), users modulate their neural population activity to move an effector towards a target, stop accurately, and correct for movement errors. We call the rules that govern this modulation a 'feedback control policy'. A better understanding of these policies may inform the design of higher-performing neural decoders. Approach. We studied how three participants in the BrainGate2 pilot clinical trial used an iBCI to control a cursor in a 2D target acquisition task. Participants used a velocity decoder with exponential smoothing dynamics. Through offline analyses, we characterized the users' feedback control policies by modeling their neural activity as a function of cursor state and target position. We also tested whether users could adapt their policy to different decoder dynamics by varying the gain (speed scaling) and temporal smoothing parameters of the iBCI. Main results. We demonstrate that control policy assumptions made in previous studies do not fully describe the policies of our participants. To account for these discrepancies, we propose a new model that captures (1) how the user's neural population activity gradually declines as the cursor approaches the target from afar, then decreases more sharply as the cursor comes into contact with the target, (2) how the user makes constant feedback corrections even when the cursor is on top of the target, and (3) how the user actively accounts for the cursor's current velocity to avoid overshooting the target. Further, we show that users can adapt their control policy to decoder dynamics by attenuating neural modulation when the cursor gain is high and by damping the cursor velocity more strongly when the smoothing dynamics are high. Significance. Our control policy model may help to build better decoders, understand how neural activity varies during active iBCI control, and produce better simulations of closed-loop iBCI movements.
机译：目的。当使用皮质内BCI（iBCI）时，用户可以调节其神经种群活动，以将效应子移向目标，准确停止并纠正运动错误。我们将控制这种调制的规则称为“反馈控制策略”。对这些策略的更好理解可以为高性能神经解码器的设计提供参考。方法。我们研究了BrainGate2试点临床试验中的三名参与者如何使用iBCI来控制2D目标获取任务中的光标。参与者使用了具有指数平滑动力学的速度解码器。通过离线分析，我们通过将用户的神经活动建模为光标状态和目标位置的函数来表征用户的反馈控制策略。我们还测试了用户是否可以通过更改iBCI的增益（速度缩放）和时间平滑参数来使其策略适应不同的解码器动态。主要结果。我们证明，先前研究中做出的控制策略假设不能完全描述参与者的策略。为了解决这些差异，我们提出了一个新模型，该模型捕获（1）用户的神经种群活动如何随着光标从远处接近目标而逐渐下降，然后随着光标与目标接触而更急剧地下降，（2）甚至当光标位于目标上方时，用户如何进行恒定的反馈校正；以及（3）用户如何主动考虑光标的当前速度以避免过大的目标。此外，我们表明，当光标增益较高时，用户可以通过衰减神经调制来使他们的控制策略适应解码器动态变化；而当平滑度动态参数较高时，可以通过更强烈地抑制光标速度来使用户的控制策略适应解码器的动态变化。意义。我们的控制策略模型可能有助于构建更好的解码器，了解神经活动在主动iBCI控制过程中如何变化，并产生更好的闭环iBCI运动模拟。

著录项

  • 来源
    《Journal of neural engineering》 |2017年第1期|016001.1-016001.16|共16页
  • 作者

    Francis R Willett; Chethan Pandarinath; Beata Jarosiewicz; Brian A Murphy; William D Memberg; Christine H Blabe; Jad Saab; Benjamin L Walter; Jennifer A Sweet; Jonathan P Miller; Jaimie M Henderson; Krishna V Shenoy; John D Simeral; Leigh R Hochberg; Robert F Kirsch; A Bolu Ajiboye;

  • 作者单位

    Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA,Louis Stokes Cleveland Department of Veterans Affairs Medical Center, FES Center of Excellence, Rehab. R&D Service, Cleveland, OH, USA;

    Department of Neurosurgery, Stanford University, Stanford, CA, USA,Department of Electrical Engineering, Stanford University, Stanford, CA, USA,Stanford Neurosciences Institute, Stanford University, Stanford, CA, USA;

    Department of Neuroscience, Brown University, Providence, RI, USA,Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Department of Veterans Affairs Medical Center, Providence, RI, USA,Brown Institute for Brain Science, Brown University, Providence, RI, USA;

    Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA,Louis Stokes Cleveland Department of Veterans Affairs Medical Center, FES Center of Excellence, Rehab. R&D Service, Cleveland, OH, USA;

    Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA,Louis Stokes Cleveland Department of Veterans Affairs Medical Center, FES Center of Excellence, Rehab. R&D Service, Cleveland, OH, USA;

    Department of Neurosurgery, Stanford University, Stanford, CA, USA;

    Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Department of Veterans Affairs Medical Center, Providence, RI, USA,School of Engineering, Brown University, Providence, RI, USA;

    Louis Stokes Cleveland Department of Veterans Affairs Medical Center, FES Center of Excellence, Rehab. R&D Service, Cleveland, OH, USA,Department of Neurology, University Hospitals Case Medical Center, Cleveland, OH, USA;

    Louis Stokes Cleveland Department of Veterans Affairs Medical Center, FES Center of Excellence, Rehab. R&D Service, Cleveland, OH, USA,Department of Neurosurgery, University Hospitals Case Medical Center, Cleveland, OH, USA;

    Louis Stokes Cleveland Department of Veterans Affairs Medical Center, FES Center of Excellence, Rehab. R&D Service, Cleveland, OH, USA,Department of Neurosurgery, University Hospitals Case Medical Center, Cleveland, OH, USA;

    Department of Neurosurgery, Stanford University, Stanford, CA, USA,Stanford Neurosciences Institute, Stanford University, Stanford, CA, USA;

    Department of Electrical Engineering, Stanford University, Stanford, CA, USA,Stanford Neurosciences Institute, Stanford University, Stanford, CA, USA,Department of Neurobiology, Stanford University, Stanford, CA, USA,Department of Bioengineering, Stanford University, Stanford, CA, USA,Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA;

    Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Department of Veterans Affairs Medical Center, Providence, RI, USA,Brown Institute for Brain Science, Brown University, Providence, RI, USA,School of Engineering, Brown University, Providence, RI, USA,Department of Neurology, Massachusetts General Hospital, Boston, MA, USA;

    Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Department of Veterans Affairs Medical Center, Providence, RI, USA,Brown Institute for Brain Science, Brown University, Providence, RI, USA,School of Engineering, Brown University, Providence, RI, USA,Department of Neurology, Massachusetts General Hospital, Boston, MA, USA,Department of Neurology, Harvard Medical School, Boston, MA, USA;

    Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA,Louis Stokes Cleveland Department of Veterans Affairs Medical Center, FES Center of Excellence, Rehab. R&D Service, Cleveland, OH, USA;

    Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA,Louis Stokes Cleveland Department of Veterans Affairs Medical Center, FES Center of Excellence, Rehab. R&D Service, Cleveland, OH, USA;

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  • 正文语种 eng
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  • 关键词

    brain-computer interface; motor cortex; motor control;

    机译：脑机接口;运动皮层;电机控制;

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