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系統識別號 U0026-0812200915255289
論文名稱(中文) 以mu波為基礎之大腦電腦介面之實現與強化
論文名稱(英文) Implementation and Enhancement of Mu-Rhythm-based Brain-Computer Interface
校院名稱 成功大學
系所名稱(中) 機械工程學系碩博士班
系所名稱(英) Department of Mechanical Engineering
學年度 97
學期 2
出版年 98
研究生(中文) 陳志瑋
研究生(英文) Chih-Wei Chen
電子信箱 n1891110@mail.ncku.edu.tw
學號 N1891110
學位類別 博士
語文別 英文
論文頁數 166頁
口試委員 召集委員-施明璋
口試委員-陳家進
口試委員-孫永年
指導教授-朱銘祥
口試委員-楊谷洋
口試委員-林昭宏
口試委員-吳育德
指導教授-林宙晴
中文關鍵字 mu波  腦波  腦電圖  BCI為基礎之手部輔具  事件相關非同步化 (ERD)  生物回饋控制  丘腦皮質數學模型 (thalamo-cortical model) 
英文關鍵字 BCI-based orthotic hand  mu rhythm  biofeedback training  event-related desynchronization (ERD)  thalamo-cortical model  electroencephalogram (EEG) 
學科別分類
中文摘要 人類的大腦是身體功能的控制中樞,除了管理情緒、思想、推理、記憶等功能外,還必須經由中樞神經系統對骨骼肌肉系統傳遞動作命令,並處理全身感覺受器所上傳之感覺訊號。但是中樞神經系統可能因為意外事故或疾病而受損, 病人的大腦因而失去部分或是完全喪失控制骨骼肌肉系統的能力。例如中風或是脊髓損傷之病人, 依病情的嚴重程度,其對自主動作之控制能力各有不同,如左側或右側身體偏癱,甚至全身癱瘓。有些病人可藉由適當的復健療程逐漸恢復,但有些病人只能依賴義肢或輔具來輔助日常生活起居所需之動作, 更不幸的病人甚至只能癱瘓在床上,無法動彈。因此如何以人工的方法取代原本中樞神經系統傳遞的路徑,使病人腦中的意志得以實現,對於病人日常生活可以提供極大的幫助,同時於受創後在心理上逐漸恢復其自信心。為了幫助此類病人,本研究以頭皮上測得之腦電圖 (electroencephalogram, EEG) 為基礎,發展之人腦電腦介面並且以實驗驗證理論之可行性。本文首先研究當受測者實際進行拇指動作時,所對應腦波 -mu波- 之變化,通稱為事件相關非同步化 (簡稱 event-related desynchronization,ERD),並比較三種訊號處理方法進行辨識 ERD,以判斷受測者是否有自主動作之正確率。發現以小波分解作前處理,之後再以類神經網路進行辨識可以得到較高的正確率,若定時更新類神經網路之參數,可以使正確率維持在70%至80%。接著比較實際拇指動作與想像動作之ERD,並將訊號處理方法簡化之後,實現即時辨識之目的。接下來將即時辨識之結果輸出成為控制命令,以燈泡之明滅作為視覺回授。實驗中發現受測者的ERD量化值參差不齊,使得即時辨識之正確率不高。因此設計一原型BCI系統,可即時量化辨識結果並顯示在螢幕,作為視覺回授介面,並對八名受測者進行長期訓練。視覺回授介面包括一個可以左右移動的小方塊,其位移可由受測者藉想像運動控制。訓練過程中,辨識器(classifier)之參數並不隨之更新,使受測者學習藉想像動作操作此BCI系統。經過共十回的訓練之後,有六名受測者可以將正確率提高至80%至90%。為了研究受測者在訓練階段中腦波之變化機制,不同於昂貴費時的 fMRI與PET等生理造影之方式,本研究採用丘腦皮質數學模型 (thalamo-cortical model) 以模擬 mu 波之 ERD 現象。經過分析後發現藉由調整模型中特定之參數(g1,g2,與 r),可以模擬出人體實驗時之腦波變化。觀察模型參數之變化,可以發現網狀細胞群(reticualr cells) 被激發之比例逐漸上升。經過訓練之後,受測者可以操作一個經由BCI控制之手部輔具。將辨識器加以更新訓練後,可使控制手部輔具之反應時間縮短至約二秒。
本研究以偵測單次拇指動作為基礎,研究腦波之辨識演算法。然後將此演算法予以簡化,以實現即時辨識單次想像動作。加入了視覺回授之後,可以提高辨識正確率,並據此建構出本研究之BCI系統。藉由訓練受測者與BCI辨識器之更新,受測者可經由BCI更有效率的控制手部義肢進行抓握物體等動作。
英文摘要 The brain is the control center of entire human body. It receives and processes the information sending from sensory systems, and generates commands to muscular-skeletal systems through the central nervous system. However, the central nervous system could be damaged from diseases and accidents, so that the patients would loss partial functions of voluntary movement partially or fully. Some functions can be restored after rehabilitation, but some functions losses are permanent. To help these patients, prostheses or orthoses are developed. The goal of this study is to investigate how to realize an EEG (electroencephalogram) -based brain-computer interface (BCI), and how to control a custom-designed orthotic hand via this artificial pathway.
The first goal of this study is to detect the event-related desynchronization (ERD) of mu rhythm induced by thumb movements. Analyses of experimental results reveal that the combination of wavelet decomposition and neural network (NN) result in better success rate. The success rates can be kept between 70% to 80% by adjusting the weights of the neural network. Then the ERD induced by voluntary movement and imagining thumb movement are compared, and the real-time detection are realized by simplifying the signal processing algorithm. Therefore, the detection results can be delivered as a command to turn a bulb on and off, which provides visual feedback to the subject. The real-time detection reveal that the ERD of mu rhythm accompanied with imaginary movement is quite low, so training procedures are necessary before the subject can operate the BCI properly. A prototype BCI is constructed, and by showing the real-time result of detection on a screen, the subjects can be trained to regulate their mu rhythm by performing purely imaginary movements. With 8 recruited subjects, 6 of them have more than 80%-90% success rates after 10-session training. In addition, a thalamo-cortical model is adopted to investigate the interconnections among the neural groups affecting the amount of ERD. By fitting the model simulations to the experimental observations, the ratio of activated reticular cells is increased with training sessions. After training, the subject can operate a custom-designed hand orthosis via the BCI. It is found that the reaction time of operating the orthotic hand can be reduced to about 2 seconds after updating the parameters of classifier.
In conclusion, using algorithms of EEG processing developed in current study, thumb movements can be detected successfully. The simplified processing algorithms can be realized to detect imaginary movements on real-time. The success rates of detecting imaginary movements can be further enhanced by visual feedback, so that the subjects can operate the hand orthosis to grasp an object via BCI more efficiently. The thalamo-cortical model can be utilized to simulate the adaptation of the subjects’ brain to the training protocol.
論文目次 Abstract ii
中文摘要 iv
Contents vi
List of Tables x
Contents of Figures xi
Nomenclatures xvi
Chapter 1 INTRODUCTION 1
1.1 Background 1
1.1.1 Voluntary Movements of Human 1
1.1.2 Orthosis and Prosthsis 1
1.1.3 Brain-computer Interface (BCI) 2
1.2 Literature Review 5
1.2.1 Recording of Brain Activities 5
1.2.2 Movement-related Brain Waves 6
1.2.3 Components of BCI 7
1.2.4 EEG Sources of BCI 10
1.3 Mathematical Model of BCI System 13
1.4 Goals of this study 14
Chapter 2 METHODS 16
2.1 Analyzing the EEG Changes that Accompany Voluntary Movement 16
2.1.1 Experimental procedures and setup for recording EEG related to thumb movement 16
2.1.2 Signal processing methods 19
2.1.3 Computing ERD/ERS Patterns 23
2.1.4 Detecting Single-trial Movement via EEG 26
2.2 Detecting Imaginary Movement via EEG 36
2.2.1 Procedures of Experiment A, B and C 36
2.2.2 Signal Processing 39
2.3 Visual Feedback BCI and Subject Training 42
2.3.1 Visual Feedback BCI 42
2.3.2 Training Protocol for Subjects of BCI 43
2.4 Simulations and Fitting of Mathematical BCI Model 46
2.4.1 Lumped Model of Thalamo-cortical Neurons 46
2.4.2 Simulated BCI Model based on Thalamo-cortical Model 52
2.4.3 Estimating the values of model parameters 57
2.4.4 Validation between simulation and experimental observation 58
2.5 Construction of a Brain-computer interface (BCI) based Orthotic Hand 59
2.5.1. Primary components of BCI-based Orthotic Hand 59
2.5.2. Construction of BCI system 61
2.5.3. Mechanical design of the orthosis 65
2.5.4. Integration of BCI and orthsis 68
2.5.5. Training protocol 70
Chapter 3 RESULTS 74
3.1 Results of Movement-related EEG 74
3.1.1 EEG Changes Related to Voluntary Movements 74
3.1.2 Extracting features of mu 汹and beta rhythms by Wavelet Decomposition 79
3.1.3 Movement detection by WDm and WDb 80
3.1.4 Movement detection by STFTm and STFTb 82
3.1.5 Success rates versus thresholds 83
3.1.6 Movement detection by WDNN 84
3.1.7 Movement detection by FTNN 85
3.1.8 Success rates and statistical analyses 88
3.2 Mu rhythm suppressions for left/right-thumb movements and imaginary movements 91
3.2.1 Experiment A: comparison of bilateral thumb movements 91
3.2.2 Experiment B: detecting imaginary movement 96
3.2.3 Experiment C: detecting imaginary movement with visual feedback 101
3.3 Visual Feedback Training for BCI Subjects 107
3.4 Simulations and Fitting of Mathematical BCI model 118
3.4.1 Thalamo-cortical Model 118
3.4.2 Simulation of BCI Model 123
3.4.3 Fitting parameters of BCI model via experimental data 127
3.5 The BCI-based hand Orthosis 133
3.5.1. Hand orthosis construction and tests 133
3.5.2. Controlling hand orthosis by imaginary movements via BCI 137
3.5.3 Training of the classifier of BCI and the subjects 140
Chapter 4 DISCUSSION 142
4.1 Thumb movement experiments 142
4.1.1 Design of 2s delay from the cue to the movement onset 142
4.1.2 Training of WDNN and FTNN 142
4.1.3 Comparing WDNN to FLDA 144
4.1.4 States of subjects 145
4.2 Voluntary, imaginary movements and visual feedback 146
4.3 Thalamo-cortical models and visual feedback training 148
4.4 BCI-controlled hand orthosis 153
Chapter 5 Conclusions 157
References 159
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