TY - JOUR
T1 - An efficient and compact compressed sensing microsystem for implantable neural recordings
AU - Zhang, Jie
AU - Suo, Yuanming
AU - Mitra, Srinjoy
AU - Chin, Sang Peter
AU - Hsiao, Steven
AU - Yazicioglu, Refet Firat
AU - Tran, Trac D.
AU - Etienne-Cummings, Ralph
PY - 2014/8
Y1 - 2014/8
N2 - 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.
AB - 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.
KW - Compressed sensing (CS)
KW - dictionary learning
KW - hardware implementation
KW - multi-electrode arrays (MEA)
UR - http://www.scopus.com/inward/record.url?scp=84905368605&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84905368605&partnerID=8YFLogxK
U2 - 10.1109/TBCAS.2013.2284254
DO - 10.1109/TBCAS.2013.2284254
M3 - Article
C2 - 25073125
AN - SCOPUS:84905368605
VL - 8
SP - 485
EP - 496
JO - IEEE Transactions on Biomedical Circuits and Systems
JF - IEEE Transactions on Biomedical Circuits and Systems
SN - 1932-4545
IS - 4
M1 - 6693746
ER -