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系統識別號 U0026-0812200911370116
論文名稱(中文) 腦波量測系統之研製與腦波信號之非線性分析
論文名稱(英文) Design and Implementation of an EEG Measurement System and the Nonlinear Analysis of EEG Signal
校院名稱 成功大學
系所名稱(中) 電機工程學系碩博士班
系所名稱(英) Department of Electrical Engineering
學年度 93
學期 2
出版年 94
研究生(中文) 湯雅雯
研究生(英文) Ya-Wen Tang
學號 n2692448
學位類別 碩士
語文別 中文
論文頁數 70頁
口試委員 口試委員-王俊智
口試委員-黃文祥
指導教授-戴政祺
口試委員-林志隆
中文關鍵字 腦波  人機介面  近似熵 
英文關鍵字 Brain Computer Interface (BCI)  Approximate Entropy  Electroencephalogram (EEG) 
學科別分類
中文摘要   人的思想中樞-大腦,蘊藏許多未知的訊息與疑問。腦波信號相較於體表其他信號(如心電信號等)而言是屬於較微弱的信號,除不易辨識外,也容易受外界干擾或雜訊影響而失真。現今腦波不僅廣泛應用在臨床診斷上,也有許多研究試圖將腦波訊號進一步解析,進而搭起一溝通橋樑讓使用者能與外界做聯繫,即所謂的大腦人機介面上。目前醫院用的腦波擷取裝置仍使用市售大型腦波機,不僅僅是昂貴的令人卻步,且對於使用在人機介面上更是相當不便。因此,本研究試圖以輕便的擷取裝置作為腦波的擷取電路,再搭配後端信號處理程式做分析,取得較為有效的特徵值以作為未來進一步應用之依據。本研究設計一個兩通道的腦波擷取電路,搭配使用9伏特電池供應電源,可同步量測腦波信號。對於腦波信號的分析,除了常用的線性時域及頻域分析方法外,本研究更利用非線性分析方法—近似熵作為評估參數,進一步觀察腦波在不同狀態下複雜度的變化。經實驗發現,動態近似熵可以有效分辨在睜眼、閉眼狀態及有無音樂的刺激下,腦波複雜度的變化情況。
英文摘要  The brain is the people’s thought center that contains a lot of unknown information and secrets. Electroencephalogram (EEG) is much weaker than other biosignals that measured from the body surface. The EEG signal can be easily affected by the environment disturbance and electromagnetic noise. It is difficult to distinguish the EEG signal from noise. The EEG has been used to the clinical diagnosis for a long time, but now there is more study focus on the brain computer interface (BCI) research. The BCI which proffer a different communication interface for people is a brand-new technique today. The EEG was acquired by the medical instruments, and those are always bulky and expensive. The portable medical instruments used to record the EEG signal for a long-term application must be small. So the aim of this project is to develop a portable battery-powered two-channel brain wave recording system and the associated analysis program. We used both linear and nonlinear methods to analysis the EEG signals. The linear analysis method used time- and frequency-domain parameters, while nonlinear analysis method used the approximate entropy (ApEn) to distinguish the complex degree of the conscious objects. Both the linear parameters and nonlinear active ApEn illustrated the variation in the complexity from thinking state to the state with music stimulus.
論文目次 中文摘要........................................i
Abstract.......................................ii
致謝..........................................iii
目錄...........................................iv
表目錄........................................vii
圖目錄.......................................viii
第一章緒論......................................1
1.1研究背景.....................................1
1.2國內外相關文獻回顧...........................2
1.3研究動機.....................................4
1.4研究目的.....................................5
第二章 研究背景.................................7
2.1 腦波簡介....................................7
2.1.1 神經細胞電位..............................7
2.1.2 腦波的產生................................8
2.1.3 腦電圖的分類..............................8
2.1.4 腦波電極記錄..............................9
2.2 腦波干擾問題...............................11
2.3 濾波器設計.................................11
2.3.1 低通濾波器...............................12
2.3.2 高通濾波器...............................13
2.3.3 帶通濾波器...............................14
2.3.4 帶拒濾波器...............................16
2.4 類比數位轉換器.............................17
2.4.1 擷取基本原理.............................17
2.4.2 類比輸入.................................18
2.5 近似熵.....................................20
2.5.1 m的選取.................................23
2.5.2 r的選取.................................24
2.5.3 N的選取..................................24
2.5.4 近似熵性質討論...........................24
2.5.5 移動視窗計算法...........................25
第三章 腦波系統設計............................27
3.1 系統方塊圖.............................27
3.2 電源供應模組...............................27
3.3 量測模組...................................28
3.3.1前端放大器................................29
3.3.2 右腿驅動電路.............................30
3.3.3高通濾波器................................31
3.3.4陷波濾波器................................33
3.3.5低通濾波器................................34
3.3.6量測系統架設..............................35
3.4 訊號處理模組...............................36
3.5 系統參數分析...............................36
3.6 實驗設計...................................37
3.6.1睜眼與閉眼實驗............................38
3.6.2音樂對腦波之影響..........................39
第四章 實驗結果................................40
4.1 電路模擬結果...............................40
4.1.1時域測試..................................40
4.1.2頻域測試..................................46
4.1.3雜訊測試..................................47
4.1.4最壞狀況分析..............................48
4.2 電路測試結果...............................49
4.2.1 時域測試.................................50
4.1.2頻域測試..................................51
4.3 腦波實際量測結果...........................52
4.4 睜眼與閉眼實驗結果.........................54
4.5 音樂實驗結果...............................58
第五章結果討論與未來發展.......................63
5.1結果討論....................................63
5.2結論........................................64
5.3未來發展....................................65
參考文獻.......................................66
自述...........................................70
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