On-chip principal component analysis with a mean pre-estimation method for spike sorting

Tung Chien Chen, Kuanfu Chen, Wentai Liu, Liang Gee Chen

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

6 Citations (Scopus)

Abstract

Principal component analysis (PCA) spike sorting hardware in an integrated neural recording system is highly desired for wireless neuroprosthetic devices. However, a large memory is required to store thousands of spike events during the PCA training procedure, which impedes the on-chip implementation for the PCA training engine. In this paper, a mean pre-estimation method is proposed to save 99.01% memory requirement by breaking the algorithm dependency. According to the simulation result, 100 dB signal-to-error power ratio can be preserved for the resulting principal components. According to the implementation result, 6.07 mm2 silicon area is required after a 283.16 mm2 area saving for the proposed PCA training hardware.

Original languageEnglish
Title of host publication2009 IEEE International Symposium on Circuits and Systems, ISCAS 2009
Pages3110-3113
Number of pages4
DOIs
Publication statusPublished - 2009
Event2009 IEEE International Symposium on Circuits and Systems, ISCAS 2009 - Taipei, Taiwan
Duration: 2009 May 242009 May 27

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
ISSN (Print)0271-4310

Other

Other2009 IEEE International Symposium on Circuits and Systems, ISCAS 2009
Country/TerritoryTaiwan
CityTaipei
Period09-05-2409-05-27

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'On-chip principal component analysis with a mean pre-estimation method for spike sorting'. Together they form a unique fingerprint.

Cite this