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

58 Scopus citations


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 languageEnglish (US)
Article number6693746
Pages (from-to)485-496
Number of pages12
JournalIEEE Transactions on Biomedical Circuits and Systems
Issue number4
StatePublished - Aug 2014
Externally publishedYes


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

ASJC Scopus subject areas

  • Biomedical Engineering
  • Electrical and Electronic Engineering


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