The Design of a Low Noise, Multi-Channel Recording System for Use in Implanted Peripheral Nerve Interfaces.
The Design of a Low Noise, Multi-Channel Recording System for Use in Implanted Peripheral Nerve Interfaces.
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DOI：
10.3390/s22093450
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发表时间：
2022-04-30
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--
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在植入式神经接口的开发中，来自外周神经的信号的记录是一个主要挑战。由于来自身体外部的干扰、其他生物电势、甚至随机噪声可能比神经信号大几个数量级，因此需要滤波器网络来衰减噪声和干扰。然而，这些网络可能会严重影响系统性能，特别是在具有多个电极袖带（MEC）的记录系统中，其中更多数量的电极导致复杂的电路。本文对两种常用的滤波网络的性能进行了形式化分析。为了获得一组易于管理的设计方程，简化了整个系统的状态方程。推导出的方程有助于设计人员为特定应用创建接口网络。记录系统的噪声，串扰和共模抑制比（CMRR）的计算作为电极阻抗，滤波器元件值和放大器规格的函数。还讨论了电极不匹配作为任何多电极系统的固有部分的影响，使用从植入绵羊的MEC获得的测量数据。这些分析的准确性，然后通过整个系统的模拟验证。结果表明，分析方程和模拟之间的良好协议。这项工作突出了理解接口电路对神经记录系统性能的影响的至关重要性。
In the development of implantable neural interfaces, the recording of signals from the peripheral nerves is a major challenge. Since the interference from outside the body, other biopotentials, and even random noise can be orders of magnitude larger than the neural signals, a filter network to attenuate the noise and interference is necessary. However, these networks may drastically affect the system performance, especially in recording systems with multiple electrode cuffs (MECs), where a higher number of electrodes leads to complicated circuits. This paper introduces formal analyses of the performance of two commonly used filter networks. To achieve a manageable set of design equations, the state equations of the complete system are simplified. The derived equations help the designer in the task of creating an interface network for specific applications. The noise, crosstalk and common-mode rejection ratio (CMRR) of the recording system are computed as a function of electrode impedance, filter component values and amplifier specifications. The effect of electrode mismatches as an inherent part of any multi-electrode system is also discussed, using measured data taken from a MEC implanted in a sheep. The accuracy of these analyses is then verified by simulations of the complete system. The results indicate good agreement between analytic equations and simulations. This work highlights the critical importance of understanding the effect of interface circuits on the performance of neural recording systems.
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