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Long-term stability of neural prosthetic control signals from silicon cortical arrays in rhesus macaque motor cortex

机译:猕猴运动皮层中硅皮质阵列的神经修复控制信号的长期稳定性

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摘要

Cortically-controlled prosthetic systems aim to help disabled patients by translating neural signals from the brain into control signals for guiding prosthetic devices. Recent reports have demonstrated reasonably high levels of performance and control of computer cursors and prosthetic limbs, but to achieve true clinical viability, the long-term operation of these systems must be better understood. In particular, the quality and stability of the electrically-recorded neural signals require further characterization. Here, we quantify action potential changes and offline neural decoder performance over 382 days of recording from four intracortical arrays in three animals. Action potential amplitude decreased by 2.4% per month on average over the course of 9.4, 10.4, and 31.7 months in three animals. During most time periods, decoder performance was not well correlated with action potential amplitude.1p > 0.05 for three of four arrays). In two arrays from one animal, action potential amplitude declined by an average of 37% over the first 2 months after implant. However, when using simple threshold-crossing events rather than well-isolated action potentials, no corresponding performance loss was observed during this time using an offline decoder. One of these arrays was effectively used for online prosthetic experiments over the following year. Substantial short-term variations in waveforms were quantified using a wireless system for contiguous recording in one animal, and compared within and between days for all three animals. Overall, this study suggests that action potential amplitude declines more slowly than previously supposed, and performance can be maintained over the course of multiple years when decoding from threshold-crossing events rather than isolated action potentials. This suggests that neural prosthetic systems may provide high performance over multiple years in human clinical trials.
机译:皮质控制的假体系统旨在通过将来自大脑的神经信号转换为引导假体设备的控制信号来帮助残疾患者。最近的报告表明,计算机光标和假肢具有相当高的性能和控制水平,但是要实现真正的临床可行性,必须更好地理解这些系统的长期操作。特别地,电记录的神经信号的质量和稳定性需要进一步的表征。在这里,我们量化了来自三只动物的四个皮层内阵列在382天的记录中动作电位的变化和离线神经解码器的性能。在三只动物的9.4、10.4和31.7个月的过程中,动作电位振幅每月平均下降2.4%。在大多数时间段内,解码器性能与动作电位振幅之间的相关性不佳。四个阵列中的三个阵列1p> 0.05)。在来自一只动物的两个阵列中,植入后头两个月的动作电位幅度平均下降了37%。但是,当使用简单的阈值穿越事件而不是隔离良好的动作电位时,在这段时间内使用离线解码器未观察到相应的性能损失。这些阵列之一有效地用于了第二年的在线修复实验。使用无线系统对一只动物进行连续记录,可以量化波形的短期显着变化,并对所有三只动物在几天之内和几天之间进行比较。总体而言,这项研究表明,动作电位振幅的下降速度比以前预期的要慢,并且在从阈值交叉事件而不是孤立的动作电位进行解码时,可以在多年的时间内保持性能。这表明神经假体系统可以在人类临床试验中提供多年的高性能。

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  • 来源
    《Journal of neural engineering》 |2011年第4期|p.045005.1-045005.11|共11页
  • 作者单位

    Department of Electrical Engineering, Stanford University, Stanford, CA, USA;

    Department of Computer Science, Stanford University, Stanford, CA, USA;

    Department of Bioengineering, Stanford University, Stanford, CA, USA,Stanford Medical School, Stanford University, Stanford, CA, USA;

    Department of Electrical Engineering, Stanford University, Stanford, CA, USA;

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

    Neurosciences Program, Stanford University, Stanford, CA, USA;

    Department of Electrical Engineering, Stanford University, Stanford, CA, USA;

    Department of Electrical Engineering, Stanford University, Stanford, CA, USA;

    Department of Electrical Engineering, Stanford University, Stanford, CA, USA;

    Department of Neurosurgery, Palo Alto Medical Foundation, Palo Alto, CA, USA;

    Department of Electrical Engineering, Stanford University, Stanford, CA, USA,Department of Bioengineering, Stanford University, Stanford, CA, USA,Neurosciences Program, Stanford University, Stanford, CA, USA;

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