# An efficient and compact compressed sensing microsystem for implantable neural recordings

Jie Zhang, Yuanming Suo, Srinjoy Mitra, Sang Peter Chin, Steven Hsiao, Refet Firat Yazicioglu, Trac D. Tran, Ralph Etienne-Cummings

Research output: Contribution to journalArticlepeer-review

## Abstract

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 μ m times 300 μ m per recording channel, and consumes 0.27 μ W operating at 20 KHz.

Original language English (US) 6693746 485-496 12 IEEE Transactions on Biomedical Circuits and Systems 8 4 https://doi.org/10.1109/TBCAS.2013.2284254 Published - Aug 2014

## Keywords

• Compressed sensing (CS)
• dictionary learning
• hardware implementation
• multi-electrode arrays (MEA)

## ASJC Scopus subject areas

• Biomedical Engineering
• Electrical and Electronic Engineering