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

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

doi:10.1109/ISCAS.2009.5118461

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

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

生物醫學工程學系

研究成果: Conference contribution

6 引文斯高帕斯（Scopus）

摘要

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.

原文	English
主出版物標題	2009 IEEE International Symposium on Circuits and Systems, ISCAS 2009
頁面	3110-3113
頁數	4
DOIs	https://doi.org/10.1109/ISCAS.2009.5118461
出版狀態	Published - 2009
事件	2009 IEEE International Symposium on Circuits and Systems, ISCAS 2009 - Taipei, Taiwan 持續時間: 2009 5月 24 → 2009 5月 27

出版系列

名字	Proceedings - IEEE International Symposium on Circuits and Systems
ISSN（列印）	0271-4310

Other

Other	2009 IEEE International Symposium on Circuits and Systems, ISCAS 2009
國家/地區	Taiwan
城市	Taipei
期間	09-05-24 → 09-05-27

All Science Journal Classification (ASJC) codes

電氣與電子工程

存取文件

10.1109/ISCAS.2009.5118461

其它文件與鏈接

引用此

@inproceedings{f38b5a6f95104dfa93033e755aa66b1f,

title = "On-chip principal component analysis with a mean pre-estimation method for spike sorting",

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.",

author = "Chen, {Tung Chien} and Kuanfu Chen and Wentai Liu and Chen, {Liang Gee}",

year = "2009",

doi = "10.1109/ISCAS.2009.5118461",

language = "English",

isbn = "9781424438280",

series = "Proceedings - IEEE International Symposium on Circuits and Systems",

pages = "3110--3113",

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note = "2009 IEEE International Symposium on Circuits and Systems, ISCAS 2009 ; Conference date: 24-05-2009 Through 27-05-2009",

}

Chen, TC, Chen, K, Liu, W & Chen, LG 2009, On-chip principal component analysis with a mean pre-estimation method for spike sorting. 於 2009 IEEE International Symposium on Circuits and Systems, ISCAS 2009., 5118461, Proceedings - IEEE International Symposium on Circuits and Systems, 頁 3110-3113, 2009 IEEE International Symposium on Circuits and Systems, ISCAS 2009, Taipei, Taiwan, 09-05-24. https://doi.org/10.1109/ISCAS.2009.5118461

TY - GEN

T1 - On-chip principal component analysis with a mean pre-estimation method for spike sorting

AU - Chen, Tung Chien

AU - Chen, Kuanfu

AU - Liu, Wentai

AU - Chen, Liang Gee

PY - 2009

Y1 - 2009

N2 - 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.

AB - 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.

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M3 - Conference contribution

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T3 - Proceedings - IEEE International Symposium on Circuits and Systems

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EP - 3113

BT - 2009 IEEE International Symposium on Circuits and Systems, ISCAS 2009

T2 - 2009 IEEE International Symposium on Circuits and Systems, ISCAS 2009

Y2 - 24 May 2009 through 27 May 2009

ER -

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

摘要

出版系列

Other

All Science Journal Classification (ASJC) codes

存取文件

其它文件與鏈接

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引用此