Compact Low-Power Cortical Recording Architecture for Compressive Multichannel Data Acquisition
Compact Low-Power Cortical Recording Architecture for Compressive Multichannel Data Acquisition
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DOI：
10.1109/tbcas.2014.2304582
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发表时间：
2014-12-01
影响因子：
5.1
通讯作者：
Schmid, Alexandre
中科院分区：
文献类型：
--
作者：
Shoaran, Mahsa;Kamal, Mahdad Hosseini;Schmid, Alexandre
本文介绍了一种面积和功率有效的方法，用于压缩记录的皮质信号在植入式系统传输之前。压缩传感的最新研究已经显示出稀疏生物信号的亚奈奎斯特采样的有希望的结果。尽管如此，这种技术的任何大规模实施都面临着由硬件强度增加引起的关键问题。就面积使用而言，在多通道系统中实现压缩感测的成本可能显著高于没有压缩的常规数据采集系统。为了解决这个问题，提出了一种新的多通道压缩传感方案，该方案利用了从传感器阵列的电极记录的信号的空间稀疏性。分析表明，使用这种方法，在很大程度上保持了功率效率，而面积开销显着减少，从而提高了功率面积积。建议的电路结构是在UMC 0.18 μ m CMOS工艺实现的。广泛的性能分析和设计优化已经完成了低噪声，紧凑和高能效的实现。模拟和随后的重建的结果表明，恢复四倍压缩的颅内EEG信号的SNR高达21.8 dB的可能性，而消耗10.5 μ W的功率内的有效面积为250 μ m × 250 μ m每个通道。
This paper introduces an area- and power-efficient approach for compressive recording of cortical signals used in an implantable system prior to transmission. Recent research on compressive sensing has shown promising results for sub-Nyquist sampling of sparse biological signals. Still, any large-scale implementation of this technique faces critical issues caused by the increased hardware intensity. The cost of implementing compressive sensing in a multichannel system in terms of area usage can be significantly higher than a conventional data acquisition system without compression. To tackle this issue, a new multichannel compressive sensing scheme which exploits the spatial sparsity of the signals recorded from the electrodes of the sensor array is proposed. The analysis shows that using this method, the power efficiency is preserved to a great extent while the area overhead is significantly reduced resulting in an improved power-area product. The proposed circuit architecture is implemented in a UMC 0.18 mu m CMOS technology. Extensive performance analysis and design optimization has been done resulting in a low-noise, compact and power-efficient implementation. The results of simulations and subsequent reconstructions show the possibility of recovering fourfold compressed intracranial EEG signals with an SNR as high as 21.8 dB, while consuming 10.5 mu W of power within an effective area of 250 mu m x 250 mu m per channel.