Rieger Robert, Schuettler Martin, Pal Dipankar, Clarke Chris, Langlois Peter, Taylor John, Donaldson Nick
Department of Electrical Engineering, National Sun Yat-sen University, Taiwan.
IEEE Trans Neural Syst Rehabil Eng. 2006 Dec;14(4):427-37. doi: 10.1109/TNSRE.2006.886731.
In this paper, we describe the design and testing of a system for recording electroneurographic signals (ENG) from a multielectrode nerve cuff (MEC). This device, which is an extension of the conventional nerve signal recording cuff, enables ENG to be classified by action potential velocity. In addition to electrical measurements, we provide preliminary in vitro data obtained from frogs that demonstrate the validity of the technique for the first time. Since typical ENG signals are extremely small, on the order of 1 1 microV, very low-noise, high-gain amplifiers are required. The ten-channel system we describe was realized in a 0.8 microm CMOS technology and detailed measured results are presented. The overall gain is 10 000 and the total input-referred root mean square (rms) noise in a bandwidth 1 Hz-5 kHZ is 291 nV. The active area is 12 mm(2) and the power consumption is 24 mW from +/-2.5 V power supplies.
在本文中，我们描述了一种用于记录来自多电极神经袖套（MEC）的神经电图信号（ENG）的系统的设计与测试。该设备是传统神经信号记录袖套的扩展，能够根据动作电位速度对ENG进行分类。除了电学测量外，我们还提供了首次从青蛙获得的体外初步数据，证明了该技术的有效性。由于典型的ENG信号极其微弱，约为1微伏量级，因此需要非常低噪声、高增益的放大器。我们所描述的十通道系统采用0.8微米CMOS技术实现，并给出了详细的测量结果。整体增益为10000，在1赫兹至5千赫兹带宽内的总输入参考均方根（rms）噪声为291纳伏。有源面积为12平方毫米，从±2.5伏电源供电时的功耗为24毫瓦。
