1/f neural noise reduction and spike feature extraction using a subset of informative samples

Zhi Yang; Linh Hoang; Qi Zhao; Edward Keefer; Wentai Liu

doi:10.1007/s10439-010-0201-5

1/f neural noise reduction and spike feature extraction using a subset of informative samples

Zhi Yang, Linh Hoang, Qi Zhao, Edward Keefer, Wentai Liu

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

2 Scopus citations

Abstract

This article describes a study on neural noise and neural signal feature extraction, targeting real-time spike sorting with miniaturized microchip implementation. Neuronal signature, noise shaping, and adaptive bandpass filtering are reported as the techniques to enhance the signal-to-noise ratio (SNR). A subset of informative samples of the waveforms is extracted as features for classification. Quantitative and comparative experiments with both synthesized and animal data are included to evaluate different feature extraction approaches. In addition, a preliminary hardware implementation has been realized using an integrated circuit.

Original language	English (US)
Pages (from-to)	1264-1277
Number of pages	14
Journal	Annals of Biomedical Engineering
Volume	39
Issue number	4
DOIs	https://doi.org/10.1007/s10439-010-0201-5
State	Published - Apr 2011

Bibliographical note

Funding Information:
The authors acknowledge the founding provided by the USA National Science Foundation through BMES-ERC and UC Lab Fee Program. The authors acknowledge the start-up grant provided by National University of Singapore. The authors are grateful to Dr. Victor Pikov, Eric Basham, Plexon, and BMES-ERC Cortical Testbed for providing in vivo neural data and suggestions. The authors acknowledge the in vivo database contributed by Gyorgy Buzsáki lab (http:// crcns.org/data-sets/hc/) and synthesized database contributed by Quian Quiroga (http://www.vis.caltech. edu/~rodri/Wave_clus/Wave_clus_home.htm).

Keywords

Action potential
Clustering
Spike feature extraction
Spike sorting

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10.1007/s10439-010-0201-5

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title = "1/f neural noise reduction and spike feature extraction using a subset of informative samples",

abstract = "This article describes a study on neural noise and neural signal feature extraction, targeting real-time spike sorting with miniaturized microchip implementation. Neuronal signature, noise shaping, and adaptive bandpass filtering are reported as the techniques to enhance the signal-to-noise ratio (SNR). A subset of informative samples of the waveforms is extracted as features for classification. Quantitative and comparative experiments with both synthesized and animal data are included to evaluate different feature extraction approaches. In addition, a preliminary hardware implementation has been realized using an integrated circuit.",

keywords = "Action potential, Clustering, Spike feature extraction, Spike sorting",

author = "Zhi Yang and Linh Hoang and Qi Zhao and Edward Keefer and Wentai Liu",

note = "Funding Information: The authors acknowledge the founding provided by the USA National Science Foundation through BMES-ERC and UC Lab Fee Program. The authors acknowledge the start-up grant provided by National University of Singapore. The authors are grateful to Dr. Victor Pikov, Eric Basham, Plexon, and BMES-ERC Cortical Testbed for providing in vivo neural data and suggestions. The authors acknowledge the in vivo database contributed by Gyorgy Buzs{\'a}ki lab (http:// crcns.org/data-sets/hc/) and synthesized database contributed by Quian Quiroga (http://www.vis.caltech. edu/~rodri/Wave_clus/Wave_clus_home.htm).",

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doi = "10.1007/s10439-010-0201-5",

language = "English (US)",

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AU - Yang, Zhi

AU - Hoang, Linh

AU - Zhao, Qi

AU - Keefer, Edward

AU - Liu, Wentai

N1 - Funding Information: The authors acknowledge the founding provided by the USA National Science Foundation through BMES-ERC and UC Lab Fee Program. The authors acknowledge the start-up grant provided by National University of Singapore. The authors are grateful to Dr. Victor Pikov, Eric Basham, Plexon, and BMES-ERC Cortical Testbed for providing in vivo neural data and suggestions. The authors acknowledge the in vivo database contributed by Gyorgy Buzsáki lab (http:// crcns.org/data-sets/hc/) and synthesized database contributed by Quian Quiroga (http://www.vis.caltech. edu/~rodri/Wave_clus/Wave_clus_home.htm).

PY - 2011/4

Y1 - 2011/4

N2 - This article describes a study on neural noise and neural signal feature extraction, targeting real-time spike sorting with miniaturized microchip implementation. Neuronal signature, noise shaping, and adaptive bandpass filtering are reported as the techniques to enhance the signal-to-noise ratio (SNR). A subset of informative samples of the waveforms is extracted as features for classification. Quantitative and comparative experiments with both synthesized and animal data are included to evaluate different feature extraction approaches. In addition, a preliminary hardware implementation has been realized using an integrated circuit.

AB - This article describes a study on neural noise and neural signal feature extraction, targeting real-time spike sorting with miniaturized microchip implementation. Neuronal signature, noise shaping, and adaptive bandpass filtering are reported as the techniques to enhance the signal-to-noise ratio (SNR). A subset of informative samples of the waveforms is extracted as features for classification. Quantitative and comparative experiments with both synthesized and animal data are included to evaluate different feature extraction approaches. In addition, a preliminary hardware implementation has been realized using an integrated circuit.

KW - Action potential

KW - Clustering

KW - Spike feature extraction

KW - Spike sorting

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JO - Annals of Biomedical Engineering

JF - Annals of Biomedical Engineering

IS - 4

ER -

1/f neural noise reduction and spike feature extraction using a subset of informative samples

Abstract

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Keywords

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