An Efficient and Compact Compressed Sensing Microsystem for Implantable Neural Recordings

摘要

Multi-Electrode Arrays (MEA) have been widely used in neuroscience experiments. However, the reduction of their wireless transmission power consumption remains a major challenge. To resolve this challenge, an efficient on-chip signal compression method is essential. In this paper, we first introduce a signal-dependent Compressed Sensing (CS) approach that outperforms previous works in terms of compression rate and reconstruction quality. Using a publicly available database, our simulation results show that the proposed system is able to achieve a signal compression rate of 8 to 16 while guaranteeing almost perfect spike classification rate. Finally, we demonstrate power consumption measurements and area estimation of a test structure implemented using TSMC 0.18 $mu$m process. We estimate the proposed system would occupy an area of around 200 $mu$m $times$300 $,mu$m per recording channel, and consumes 0.27 $mu$ W operating at 20 KHz .