Detection and characterization of neural spikes

Hsiao Lung Chan; Ming An Lin; Yu Li Wu; Hsin Yi Lai; Shih Tseng Lee; Jin Fu Yang; Shih Chin Fang

doi:10.1049/cp:20060380

Detection and characterization of neural spikes

Hsiao Lung Chan^*, Ming An Lin, Yu Li Wu, Hsin Yi Lai, Shih Tseng Lee, Jin Fu Yang, Shih Chin Fang

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

In this paper the characteristics of neural spikes in deep brain recording were investigated. The adaptive spike thresholding was used to detect the neural spikes, and the autoregressive model was proposed to differentiate neural spikes and background potentials. Our preliminary results indicated there appears a higher firing rate, more concentrated inter-spike interval histogram and more correlated spike train in the neural signal recorded in substantia Nigra compared to the signals in reticulate subthalamic nucleus.

Original language	English
Title of host publication	IET 3rd International Conference MEDSIP 2006
Subtitle of host publication	Advances in Medical, Signal and Information Processing
Pages	56
Number of pages	1
Edition	520
DOIs	https://doi.org/10.1049/cp:20060380
State	Published - 2006
Event	IET 3rd International Conference MEDSIP 2006: Advances in Medical, Signal and Information Processing - Glasgow, United Kingdom Duration: 17 07 2006 → 19 07 2006

Publication series

Name	IET Conference Publications
Number	520

Conference

Conference	IET 3rd International Conference MEDSIP 2006: Advances in Medical, Signal and Information Processing
Country/Territory	United Kingdom
City	Glasgow
Period	17/07/06 → 19/07/06

Keywords

Autocorrelation
Autoregressive model
Firing rate
Inter-spike interval
Microelectrode recording
Spike detection

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10.1049/cp:20060380

Cite this

@inproceedings{d085a3e505914b0fa533f924694279f8,

title = "Detection and characterization of neural spikes",

abstract = "In this paper the characteristics of neural spikes in deep brain recording were investigated. The adaptive spike thresholding was used to detect the neural spikes, and the autoregressive model was proposed to differentiate neural spikes and background potentials. Our preliminary results indicated there appears a higher firing rate, more concentrated inter-spike interval histogram and more correlated spike train in the neural signal recorded in substantia Nigra compared to the signals in reticulate subthalamic nucleus.",

keywords = "Autocorrelation, Autoregressive model, Firing rate, Inter-spike interval, Microelectrode recording, Spike detection",

author = "Chan, {Hsiao Lung} and Lin, {Ming An} and Wu, {Yu Li} and Lai, {Hsin Yi} and Lee, {Shih Tseng} and Yang, {Jin Fu} and Fang, {Shih Chin}",

year = "2006",

doi = "10.1049/cp:20060380",

language = "英语",

isbn = "0863416586",

series = "IET Conference Publications",

number = "520",

pages = "56",

booktitle = "IET 3rd International Conference MEDSIP 2006",

edition = "520",

note = "IET 3rd International Conference MEDSIP 2006: Advances in Medical, Signal and Information Processing ; Conference date: 17-07-2006 Through 19-07-2006",

}

Chan, HL, Lin, MA, Wu, YL, Lai, HY, Lee, ST, Yang, JF & Fang, SC 2006, Detection and characterization of neural spikes. in IET 3rd International Conference MEDSIP 2006: Advances in Medical, Signal and Information Processing. 520 edn, IET Conference Publications, no. 520, pp. 56, IET 3rd International Conference MEDSIP 2006: Advances in Medical, Signal and Information Processing, Glasgow, United Kingdom, 17/07/06. https://doi.org/10.1049/cp:20060380

TY - GEN

T1 - Detection and characterization of neural spikes

AU - Chan, Hsiao Lung

AU - Lin, Ming An

AU - Wu, Yu Li

AU - Lai, Hsin Yi

AU - Lee, Shih Tseng

AU - Yang, Jin Fu

AU - Fang, Shih Chin

PY - 2006

Y1 - 2006

N2 - In this paper the characteristics of neural spikes in deep brain recording were investigated. The adaptive spike thresholding was used to detect the neural spikes, and the autoregressive model was proposed to differentiate neural spikes and background potentials. Our preliminary results indicated there appears a higher firing rate, more concentrated inter-spike interval histogram and more correlated spike train in the neural signal recorded in substantia Nigra compared to the signals in reticulate subthalamic nucleus.

AB - In this paper the characteristics of neural spikes in deep brain recording were investigated. The adaptive spike thresholding was used to detect the neural spikes, and the autoregressive model was proposed to differentiate neural spikes and background potentials. Our preliminary results indicated there appears a higher firing rate, more concentrated inter-spike interval histogram and more correlated spike train in the neural signal recorded in substantia Nigra compared to the signals in reticulate subthalamic nucleus.

KW - Autocorrelation

KW - Autoregressive model

KW - Firing rate

KW - Inter-spike interval

KW - Microelectrode recording

KW - Spike detection

UR - http://www.scopus.com/inward/record.url?scp=34047173588&partnerID=8YFLogxK

U2 - 10.1049/cp:20060380

DO - 10.1049/cp:20060380

M3 - 会议稿件

AN - SCOPUS:34047173588

SN - 0863416586

SN - 9780863416583

T3 - IET Conference Publications

SP - 56

BT - IET 3rd International Conference MEDSIP 2006

T2 - IET 3rd International Conference MEDSIP 2006: Advances in Medical, Signal and Information Processing

Y2 - 17 July 2006 through 19 July 2006

ER -

Detection and characterization of neural spikes

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Keywords

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