Communication Channel Analysis and Real Time Compressed Sensing for High Density Neural Recording Devices

Jie Zhang, Kerron Duncan, Yuanming Suo, Tao Xiong, Srinjoy Mitra, Trac Duy Tran, Ralph Etienne-cummings

Research output: Contribution to journalArticlepeer-review

Abstract

Next generation neural recording and brain-machine interface (BMI) devices call for high density or distributed systems with more than 1000 recording sites. As the recording site density grows, the device generates data on the scale of several hundred megabits per second (Mbps). Transmitting such large amounts of data induces significant power consumption and heat dissipation for the implanted electronics. Facing these constraints, efficient on-chip compression techniques become essential to the reduction of implanted systems power consumption. This paper analyzes the communication channel constraints for high density neural recording devices. This paper then quantifies the improvement on communication channel using efficient on-chip compression methods. Finally, This paper describes a Compressed Sensing (CS) based system that can reduce the data rate by > 10× times while using power on the order of a few hundred nW per recording channel.
Original languageUndefined/Unknown
Pages (from-to)599-608
Number of pages10
Journal IEEE Transactions On Circuits and Systems Part I - Regular Papers
Volume63
Issue number5
Early online date12 May 2016
DOIs
Publication statusPublished - 28 Jun 2016

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