Suppr超能文献

一种数字辅助的信号折叠神经记录放大器。

A digitally assisted, signal folding neural recording amplifier.

作者信息

Chen Yi, Basu Arindam, Liu Lei, Zou Xiaodan, Rajkumar Ramamoorthy, Dawe Gavin Stewart, Je Minkyu

出版信息

IEEE Trans Biomed Circuits Syst. 2014 Aug;8(4):528-42. doi: 10.1109/TBCAS.2013.2288680.

Abstract

A novel signal folding and reconstruction scheme for neural recording applications that exploits the 1/f(n) characteristics of neural signals is described in this paper. The amplified output is 'folded' into a predefined range of voltages by using comparison and reset circuits along with the core amplifier. After this output signal is digitized and transmitted, a reconstruction algorithm can be applied in the digital domain to recover the amplified signal from the folded waveform. This scheme enables the use of an analog-to-digital convertor with less number of bits for the same effective dynamic range. It also reduces the transmission data rate of the recording chip. Both of these features allow power and area savings at the system level. Other advantages of the proposed topology are increased reliability due to the removal of pseudo-resistors, lower harmonic distortion and low-voltage operation. An analysis of the reconstruction error introduced by this scheme is presented along with a behavioral model to provide a quick estimate of the post reconstruction dynamic range. Measurement results from two different core amplifier designs in 65 nm and 180 nm CMOS processes are presented to prove the generality of the proposed scheme in the neural recording applications. Operating from a 1 V power supply, the amplifier in 180 nm CMOS has a gain of 54.2 dB, bandwidth of 5.7 kHz, input referred noise of 3.8 μVrms and power dissipation of 2.52 μW leading to a NEF of 3.1 in spike band. It exhibits a dynamic range of 66 dB and maximum SNDR of 43 dB in LFP band. It also reduces system level power (by reducing the number of bits in the ADC by 2) as well as data rate to 80% of a conventional design. In vivo measurements validate the ability of this amplifier to simultaneously record spike and LFP signals.

摘要

本文描述了一种用于神经记录应用的新型信号折叠与重构方案,该方案利用了神经信号的1/f(n)特性。通过使用比较和复位电路以及核心放大器,将放大后的输出“折叠”到预定义的电压范围内。在该输出信号数字化并传输之后,可以在数字域中应用重构算法,从折叠波形中恢复放大后的信号。对于相同的有效动态范围,该方案能够使用位数更少的模数转换器。它还降低了记录芯片的传输数据速率。这两个特性都有助于在系统层面节省功耗和面积。所提出拓扑结构的其他优点包括:由于去除了伪电阻而提高了可靠性、降低了谐波失真以及实现了低电压操作。本文还对该方案引入的重构误差进行了分析,并给出了一个行为模型,以快速估计重构后的动态范围。给出了在65纳米和180纳米CMOS工艺中两种不同核心放大器设计的测量结果,以证明所提方案在神经记录应用中的通用性。工作在1V电源下,180纳米CMOS工艺的放大器增益为54.2dB,带宽为5.7kHz,输入参考噪声为3.8μVrms,功耗为2.52μW,在尖峰频段的噪声等效因子为3.1。在局部场电位(LFP)频段,它的动态范围为66dB,最大信噪失真比(SNDR)为43dB。它还降低了系统级功耗(通过将模数转换器的位数减少2位)以及数据速率,降至传统设计的80%。体内测量验证了该放大器同时记录尖峰信号和LFP信号的能力。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验