臺灣博碩士論文加值系統

神經科學、傳感器技術和高效信號處理演算法的顯著進步，大大促進了從實驗室導向的神經科學研究轉換至實際應用。腦機系統（BCI）代表將大腦信號轉化為可操作決策的主要步驟，並且主要由訊號處理和訊號分析組成引導使用者和系統之間的通信。本文介紹電路設計和幾種目前的類神經網路技術與計算智能方法應用於基於腦波之腦機系統。在前端訊號處理方面，新型可攜式腦電圖裝置搭配乾式電極作為傳統腦機系統搭配濕電極和其龐大尺寸的替代物。同時，在後端訊號分析方面，引入模糊類神經網路和信息融合技術來分別解決複雜腦網路描述和決策融合的技術問題。例如，信息融合技術已被用於處理在真實世界環境中運動想像應用的個體差異問題。本論文也介紹了使用一新潁眼電圖（EOG）信號分類方法的腦機系統，提供了另一種人與機器之間的通信方式。隨著不斷改進的發展一方便的方法記錄大腦信號與提取關於意圖的知識，腦機系統技術被設想為在不久的將來引起廣泛的實際應用。

Significant advances in neuroscience, sensor technologies, and efficient signal processing algorithms have greatly facilitated the transition from laboratory-oriented neuroscience research to practical applications. Brain-computer interfaces (BCIs) represent major strides in translating brain signals into actionable decisions and primarily consist of front-end signal processing and back-end signal analyzing that guide the communications between users and systems. This dissertation presents several current neuro network technologies and computational intelligence methods applied to EEG-based BCIs. In the front-end signal processing aspect, novel portable EEG devices featuring dry electrodes are introduced as substitutes for traditional BCIs with wet electrodes and its bulky size. Meanwhile, in the back-end signal analyzing aspect, fuzzy neural networks and information fusion techniques are introduced to address the technical issues of complex brain network description, and decision fusion, respectively. For instance, information fusion technique has been utilized to attack the individual differences problem of motor imagery applications in the real-world environment. This dissertation also presents BCIs system with a novel classification method that uses electrooculography (EOG) signals, which provide another communication way between humans and machine. With continuous improvements in the development of a convenient approach to record brain signals and extract knowledge regarding intentions, BCI techniques are envisioned to lead to a wide range of real-life applications in the near future.

摘要 i
Abstract ii
誌謝 iii
Table of Content v
List of Tables x
List of Figures xi
Chapter 1 Introduction 1
1.1 Background 1
1.2 Statement of the Problem 2
1.3 Aim of the Study and Organization of the Dissertation 4
Chapter 2 EEG-BASED NEUROIMAGING TECHNOLOGY FOR BRAIN-COMPUTER INTERFACES 6
2.1 System Architecture 6
2.2 EEG Signal Processing 7
2.3 Circuit Design 8
2.3.1 Preamplifier 8
2.3.2 Analog to Digital Converter 10
2.3.3 Micro Controller Unit 10
2.3.4 Wireless Transmission Unit 11
2.3.5 Power Supply 11
2.4 Results 12
2.4.1 Verification 12
2.4.2 Performance 15
2.5 Discussion 19
2.6 Conclusions 19
Chapter 3 EEG-BASED BRAIN-COMPUTER INTERFACE IN PREDICTING DRIVING FATIGUE USING FUZZY NEURAL NETWORK 21
3.1 Background 21
3.2 Structure of Recurrent Self-Evolving Fuzzy Neural Network 23
3.2.1 Structure Learning 26
3.2.2 Parameter Learning 27
3.3 Experiment Setup 29
3.3.1 Virtual-reality-based Highway Experimental Environment 29
3.3.2 Participants 30
3.3.3 Driving Fatigue Paradigm 31
3.4 Experiment Results and Discussion 33
3.4.1 Results 33
3.4.2 Discussion 36
3.5 Conclusions 38
Chapter 4 USING MOTOR IMAGERY BASED BRAIN-COMPUTER INTERFACE WITH FUZZY FUSION SIGNAL PROCESSING STRAGERY 40
4.1 Background 40
4.2 Structure of Fuzzy Fusion 43
4.3 EEG Processing and Categorization 44
4.3.1 Common Spatial Pattern and Linear Discriminant Analysis 44
4.3.2 Fuzzy Integral 45
4.3.3 Particle Swarm Optimization 47
4.4 Experiment Setup 49
4.4.1 Experimental Procedure 50
4.5 Results and Discussion 51
4.5.1 Fuzzy Fusion Performance 51
4.5.2 The Proposed Online BCI System and Its Application 53
4.5.3 Reliability Test 55
4.6 Conclusions 55
Chapter 5 Controlling a Brain-Computer Interface System with a Novel Classification Method that uses Electrooculography Signals 57
5.1 Background 57
5.2 System Architecture 60
5.3 EOG Signal Processing 61
5.3.1 Normalization Factor Calibration 62
5.3.2 EOG Signal Preprocessing 63
5.3.3 Feature Extraction 65
5.3.4 EOG Signal Classifiers 65
5.4 Results 67
5.4.1 Subjects and Experiments 67
5.4.2 Experimental Procedure 68
5.4.3 Performance of the Eye movement Feature and Movement Classification 69
5.5 Discussions 74
5.5.1 Error Movement in Oblique Direction 74
5.5.2 Eye Movements in the Vertical Direction 76
5.6 Conclusions 76
Chapter 5 Summary and Future Works 78
Reference 79
Publication 97

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