IEEE Trans Biomed Circuits Syst. 2018 Feb;12(1):13-23. doi: 10.1109/TBCAS.2017.2750484.
Multiplexing is an important strategy in multichannel acquisition systems. The per-channel antialiasing filters needed in the traditional multiplexing architecture limit its scalability for applications requiring high channel density, high channel count, and low noise. A particularly challenging example is multielectrode arrays for recording from neural systems. We show that conventional approaches must tradeoff recording density and noise performance, at a scale far from the ideal goal of one-to-one mapping between neurons and sensors. We present a multiplexing architecture without per-channel antialiasing filters. The sparsely sampled data are recovered through a compressed sensing strategy, involving statistical reconstruction and removal of the undersampled thermal noise. In doing so, we replace large analog components with digital signal processing blocks, which are much more amenable to scaled CMOS implementation. The resulting statistically reconstructed multiplexing architecture recovers input signals at significantly improved signal-to-noise ratios when compared to conventional multiplexing with antialiasing filters at the same per-channel area. We implement the new architecture in a 65 536-channel neural recording system and show that it is able to recover signals with performance comparable to conventional high-performance, single-channel systems, despite a more than four-orders-of-magnitude increase in channel density.
多路复用是多通道采集系统中的一种重要策略。传统多路复用架构中所需的每通道抗混叠滤波器限制了其在需要高通道密度、高通道数和低噪声的应用中的可扩展性。一个特别具有挑战性的例子是用于记录神经系统的多电极阵列。我们表明，传统方法必须在记录密度和噪声性能之间进行权衡，而在远未达到神经元和传感器之间一对一映射的理想目标的情况下。我们提出了一种没有每通道抗混叠滤波器的多路复用架构。通过压缩感知策略恢复稀疏采样数据，该策略涉及统计重建和去除欠采样热噪声。通过这样做，我们用数字信号处理块代替了大型模拟组件，数字信号处理块更适合按比例实现 CMOS。与具有相同每通道面积的抗混叠滤波器的传统多路复用相比，所得到的经过统计重建的多路复用架构在显著提高的信噪比下恢复输入信号。我们在一个 65536 通道的神经记录系统中实现了新架构，并表明尽管通道密度增加了四个数量级以上，但它仍能够恢复与传统高性能单通道系统相当的性能。
