A streaming PCA based VLSI chip for neural data compression

Tong Wu, Wenfeng Zhao, Hongsun Guo, Hubert Lim, Zhi Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Local field potentials (LFPs) are popularly used in wireless neural interface due to its chronic stability and robustness against noise and radio interferences. On-chip data compression is advantageous that allows for integration with the recent low-power, low-data-rate wireless technologies to ensure reliable operations. In this paper, we propose a streaming principal component analysis (PCA) based algorithm and its microchip implementation to compress multichannel LFP data. The chip has been designed in a 65nm CMOS technology and occupies a silicon area of 0.06mm2. It has been tested with Guinea pig auditory cortex data recorded with a multi-shank Neuro Nexus probe, where the chip can achieve an 8× compression ratio with ∼3% average reconstruction error, consuming 144nW per channel at a 0.5V power supply.

Original languageEnglish (US)
Title of host publicationProceedings - 2016 IEEE Biomedical Circuits and Systems Conference, BioCAS 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages192-195
Number of pages4
ISBN (Electronic)9781509029594
DOIs
StatePublished - 2016
Event12th IEEE Biomedical Circuits and Systems Conference, BioCAS 2016 - Shanghai, China
Duration: Oct 17 2016Oct 19 2016

Publication series

NameProceedings - 2016 IEEE Biomedical Circuits and Systems Conference, BioCAS 2016

Other

Other12th IEEE Biomedical Circuits and Systems Conference, BioCAS 2016
CountryChina
CityShanghai
Period10/17/1610/19/16

Fingerprint Dive into the research topics of 'A streaming PCA based VLSI chip for neural data compression'. Together they form a unique fingerprint.

Cite this