簡易檢索 / 詳目顯示

研究生: 吳國璿
KOU-HSUAN,WU
論文名稱: 應用於棘波分類之棘波偵測硬體架構 在FPGA之實現
指導教授: 黃文吉
Hwang, Wen-Jyi
學位類別: 碩士
Master
系所名稱: 資訊工程學系
Department of Computer Science and Information Engineering
論文出版年: 2013
畢業學年度: 101
語文別: 中文
論文頁數: 49
中文關鍵詞: 棘波偵測
英文關鍵詞: FPGA, Normalized Correlator
論文種類: 學術論文
相關次數: 點閱:318下載:4
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  • 本論文的目的是提出一個應用在有雜訊的環境下進行即時棘波偵測之新型VLSI架構。此架構是基於 nomalized correlator 的設計,用以提升偵測效能。在計算正規化關聯值 (correlation) 之前,我們會先將棘波序列中的區段(segment)做單位化 (nomalized) 的計算,這樣做可以讓我們計算出來的正規化關聯值不受棘波序列訊號的大小及雜訊大小的干擾,皆在一個範圍值內。這樣一來,即使我們在SNR變低的環境下,也可以很容易選擇一個閥值(threshold)有效的進行棘波偵測。

    附圖目錄 ………………………………………………………………………iv 附表目錄 ………………………………………………………………………v 第一章 緒論……………………………………………………………………1 1.1 研究背景與動機…………………………………………………… 1 1.2 研究目的與方法…………………………………………………… 2 第二章 棘波偵測之研究背景與演算法則……………………………………5                   2.1 棘波分類的介紹…………………………………………………… 5 2.2 棘波偵測演算法則………………………………………………… 6 第三章 棘波偵測系統架構………………………………………………… 10 3.1 Filter Unit……………………………………………………… 12 3.2 Block Energy Computation Unit……………………………… 13 3.3 Correlator Unit………………………………………………… 15 3.4 Thresholding Unit……………………………………………… 17 第四章 實驗結果與數據討論……………………………………………… 18 4.1 實驗環境介紹………………………………………………………18 4.2 實驗數據的呈現與討論……………………………………………20 第五章 結論………………………………………………………………… 47 參考著作……………………………………………………………………… 48

    [1] S. Gibson, J. W. Judy, and D. Markovic, Spike sorting: the first step in decoding the brain, IEEE Signal Processing Magazine, pp.124-143, 2012.

    [2] M.A. Lebedev and M.A.L. Nicolelis, “Brainmachine interfaces: past, present and future,” Trends in Neurosciences, Vol.29, pp.536-546, 2006.

    [3] S. Mukhopadhyay and G. C. Ray, A new interpretation of nonlinear energy operator and its efficac y in spike detection, IEEE Trans. Biomed. Eng., Vol. 45, pp. 180187, 1998.

    [4] K. Kim and S. Kim, A wavelet-based method for action potential detection from extracel-lular neural signal recording with low signal-to-noise ratio, IEEE Trans. Biomed. Eng., Vol.50, pp. 999-l011, 2003.

    [5] N. Mtetwa, L. S. Smith, Smoothing and thresholding in neuronal spike detection, Neuro-computing, Vol. 69, pp. 1366-1370, 2006.

    [6] J. Drolet, H. Semmaoui, and M. Sawan, Low-power energy-Based CMOS digital detector for neural recording arrays, IEEE Biomedical circuits and systems conference, pp.13-16,2011.

    [7] K. Oweiss and M. Aghagolzadeh, Detection and classification of extracellular action po-tential recordings, Chapter 2 of Statistical Signal Processing for
    Neuroscience, pp.15-74,2010.

    [8] S. Gibson, J. W. Judy, and D. Markovic, An FPGA-based platform for accelerated offline spike sorting, Journal of Neuroscience Methods, Vol. 215, pp. 1-11, 2013.

    [9] L. S. Smith and N. Mtetwa, “A tool for synthesizing spike trains with realistic interference,” Vol. 159, pp.170-180, Journal of Neuroscience Methods, 2007.

    [10] S. Barati and A. Sodagar, “Discrete-time automatic spike detection circuit for neural recording implants,” Electronics Letters, vol. 47, no. 5,pp.306–307, Mar. 2011.
    [11] H. Li and Q. Xu, “Sub-threshold-based ultra-low-power neural spike detector,” Electronics Letters, vol. 47, no. 6, pp. 367–368, Mar. 2011.

    [12]J. Drolet, H. Semmaoui and M. Sawan “Low-Power Energy-Based CMOS Digital Detector for Neural Recording Arrays, ” in Biomedical Circuits and Systems Conference (BioCAS), 2011 IEEE,pp.13-16, 10-12 Nov. 2011.

    [13] W. Reichert, “Indwelling neural implants: Stategies for contending with the in vivo environment,” in BMI-Related Thermal Studies. Boca Raton, FL: CRC, 2007.

    下載圖示
    QR CODE