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

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4 Author(s)
Tung-Chien Chen ; Nat. Taiwan Univ., Taipei, Taiwan ; Kuanfu Chen ; Wentai Liu ; Liang-Gee Chen

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.

Published in:

Circuits and Systems, 2009. ISCAS 2009. IEEE International Symposium on

Date of Conference:

24-27 May 2009