Spike sorting by a minimax reduced feature set based on finite differences

Chien Chang Yen; Wei Chang Shann; Chen Tung Yen; Meng Li Tsai

doi:10.1007/s12576-008-0010-x

Spike sorting by a minimax reduced feature set based on finite differences

Chien Chang Yen, Wei Chang Shann, Chen Tung Yen, Meng Li Tsai

Graduate Institute of Learning and Instruction

Research output: Contribution to journal › Article › peer-review

Abstract

Spikes are classified according to their finite differences in various orders. The fundamental idea that makes it work is that finite differences can extract and isolate features from spikes. This method showed better sorting quality and involved less labor than the methods of principal component analysis, original reduced feature set, and wavelet-based spike classifiers.

Original language	English
Pages (from-to)	143-147
Number of pages	5
Journal	Journal of Physiological Sciences
Volume	59
Issue number	2
DOIs	https://doi.org/10.1007/s12576-008-0010-x
State	Published - Mar 2009

Keywords

Multi-channel recording
Reduced feature set
Spike sorting

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10.1007/s12576-008-0010-x

Cite this

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abstract = "Spikes are classified according to their finite differences in various orders. The fundamental idea that makes it work is that finite differences can extract and isolate features from spikes. This method showed better sorting quality and involved less labor than the methods of principal component analysis, original reduced feature set, and wavelet-based spike classifiers.",

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author = "Yen, {Chien Chang} and Shann, {Wei Chang} and Yen, {Chen Tung} and Tsai, {Meng Li}",

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Spike sorting by a minimax reduced feature set based on finite differences

Abstract

Keywords

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