臺灣博碩士論文加值系統

本論文裡我們使用獨立成份分析法(ICA)和減法聚類分類能夠有效率的分析老鼠的腦皮質層電生理訊號(ENG，腦神經電圖)。系統中我們發展一套完整的系統能夠紀錄八通道的腦神經電圖，此八通道微電極陣列被植入實驗中老鼠大腦皮質層內的主運動區，訊號經由前級放大電路與後級帶通濾波放大電路來放大，並透過A/D 卡把腦電生理訊號存取到電腦。當紀錄的同時，系統同步攝影監控紀錄老鼠的動作。紀錄腦神經訊號之後，我們使用ICA方法分析最可能獨立的原始腦電生理訊號成份。但是所得到的獨立原始訊號成份會有幾個問題存在，其獨立原訊號成份的排序、比重和大小都不能被確定，因此我們發展了源訊號關聯匹配法來解決此問題，並且使用減法聚類分類法來分類老鼠的腦神經電訊號。從實驗結果中已我們能夠有意義的分辯出老鼠做動作時與腦波訊號之間的關係。

In the study, we propose an effective method using ICA and subtractive clustering to analyze (Electroneurography) ENG signal of the rat. We have completed the system which can record 8-channel ENG signal. Using the 8-channel multi-electrode array and then implant it into the primary motor cortex of rats. The signal is amplified through front-stage amplitude circuit and after-stage amplitude band-pass filter circuit. And, we record the ENG signal into PC through A/D card. When recording the ENG signal, we also synchronously monitor the activity images of rats at the same time. After recording we employ to ICA method, we get independent sources as possible as we can. But the independent sources still have some problems. It is difficult to determine the order, weight, and the scaling of independent sources. We develop a matching source method to solve this problem, and we used the subtractive clustering which can classify the ENG signal of rats. The result we can meaningfully distinguish relation between the ENG signal and the activity of rats.

摘　要 i
ABSTRACT ii
誌謝 iv
CONTENTS v
LIST OF TABLES vii
LIST OF FIGURES ix
Chapter 1 INTRODUCTION 1
1.1 Motivation 1
1.2 Objects of research 1
1.3 Survey 2
1.3.1 Survey of experimental-design 2
1.3.2 Survey kinds of Brain Signal 3
1.3.3 Survey of capture circuit system 5
1.4 Overview of the proposed methods 5
1.5 Organization of thesis 5
Chapter 2 INDEPENDENT COMPONENT ANALYSIS 7
2.1 Blind source separation 8
2.1.1 Observing mixtures of unknown signals 8
2.1.2 Source separation based on independence 11
2.2 Independent component analysis 12
2.2.1 Definitions of linear independent component analysis 12
2.2.2 Restrictions in ICA 14
2.2.3 Preprocessing for ICA 15
2.2.3.1 Centering 15
2.2.3.2Whitening 16
2.2.4 Objective functions and algorithms 17
2.2.5 Measure of non-gaussuanity 18
2.2.5.1 Kurtosis and its properties 18
2.2.5.2 Negentropy 20
2.2.5.3 FastICA 22
Chapter 3 INDEPENDENT SOURCE SIGNAL RELATION MATCHING ALGORITHM 23
3.1 Reviewing Problem of ICA 23
3.2 Using ICA on Experiment system 24
3.3 Independent Source Signal Relation Matching Algorithm 26
3.4 Classification Nth Action Relation by Using Subtractive Clustering 31
Chapter 4 SYSTEM ARCHITECTURE 39
4.1 System Overview 39
4.2 Cortex Signal Capture System 41
4.2.1 Capture Circuits 41
4.2.1.1 DC Power Supply Circuit 41
4.2.1.2 Front-Stage Amplifier Circuit 42
4.2.1.3 Afterward- Stage Amplifier Circuit 44
4.2.1.4 Capture circuits testing and verifying 45
4.2.2 Signals digitization 46
4.2.3 Synchronous image monitor 48
Chapter 5 EXPERIMENTAL METHODS 50
5.1 Microelectrode Design and Implementation 50
5.2 The Operation Methods 52
5.2.1 The Brain of Rat 52
5.3 Rat Carrying Front-Stage Amplifier Circuit method 57
Chapter 6 EXPERIMENTS RESULT 58
6.1 The rat’s action 58
6.2 The experimental results of the action after Butterworth filter and FFT 59
6.3 The experimental results and statistical analysis 63
6.3.1 Analysis steps 63
6.3.2 The cluster result of three actions of rat-1 64
6.3.3 The cluster result of three actions of rat-2 and rat-3 65
6.4 Discussion 67
Chapter 7 CONCLUSIONS AND FUTURE WORK 68
7.1 Conclusions 68
7.2 Future work 68
REFERENCE 69
APPENDIX 71
Appendix A.1 Experiment data result of rat-1 72
作者簡介 94

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