臺灣博碩士論文加值系統

有一些重障者，例如:漸凍人或是運動神經元病變者，他們都有移動肢體動作的困難，包括走路、吃飯或抓取動作等。這些重障者需要一些高科技輔具來幫助他們進行每天的活動，例如溝通、娛樂及餵食等。腦機介面技術可能是一種有效率、有助益的技術之一，可視為一種新穎的輔具，以來協助這些重障者解決日常生活的活動問題。
在腦機介面常用的方法之一，稱為穩態視覺誘發電位(steady state visual evoked potential, SSVEP)，在本論文中，被討論並實現在文書拼寫系統、娛樂裝置及機器人的控制系統上。在穩態視覺誘發電位腦機介面的應用過程中，受測者被要求注視數個閃爍中的視覺刺激的其中一個，以選擇所想要的命令選項。當受測者注視想選的視覺刺激選項時，受測者的腦波會經由其大腦枕葉處被量測及分析。此處所量得的受測者腦波會有與所選擇的視覺刺激選項產生同樣類似的頻譜諧波響應。自受測者所量得的腦波原始訊號，經由我們發展的演算法加以預處理及辨識後，可獲得與所選視覺刺激選項相關的腦波特徵，以作為腦機介面決策模型的輸入，完成辨識與控制應用。
在本研究中，穩態視覺誘發電位的方法被應用於數(4)種不同腦機介面的應用。其中穩態視覺誘發電位腦機介面在拚寫系統的應用中，證實加入虛擬字元的熵編碼演算法可產生42.62位元/每分鐘的資訊傳輸率，比平衡結構熵編碼演算法所得到的31.9位元/每分鐘資訊傳輸率還高。其次，穩態視覺誘發電位腦機介面在控制飛天魚的應用中，利用實現模糊追蹤控制演算法作為決策模式，所得到的辨識率達96.97%，相較於作為基本規範相關分析(canonical correlation analysis, CCA)所得到的辨識率94.49%，效果更佳。另應用於控制移動機器人而自行發展的模糊特徵臨界演算法，則可產生20.57位元/每分鐘的資訊傳輸率。最後，具有傅立葉轉換分析的模糊決策模型以及振幅平方相關(magnitude squared coherence, MSC)所得到特徵輸入，應用到單一視覺刺激測試(SVST)與多視覺刺激測試(MVST)之實驗結果，分別產生F參數的分數，分別為0.5889和0.6038，但利用規範相關分析法(CCA)所得到的F參數分數，在單一視覺刺激測試與多視覺刺激測試之實驗結果，分別僅為0.4052與0.4005。基於以上各項實驗結果，，可以證明採用本研究所提出幾種演算法的腦機介面，確可成功地適用於拚寫系統、飛天魚玩具、移動機器人及協助餵食機器人等系統。

Some people with severe disability, for example, people with amyotrophic lateral sclerosis/motor neuron disease (ALS/MND), have difficulties with moving some parts of their bodies including walking, eating, grasping, etc. These people with severe disability need some high technology assistive devices to help them perform some daily activities such as communicating, entertainment and self-feeding. Brain-computer interface (BCI) could be one of the efficient, helpful technologies and can be utilized as a novel assistive device to help these people with severe disability to perform these daily activities.
One of the BCI related methods called steady state visual evoked potential (SSVEP) is implemented and discussed in some BCI applications including English spelling system, recreational device and robot control in this thesis. In SSVEP-based BCI applications, the subjects were asked to focus their attention on one of the several flickering visual stimuli to choose the desired command. While the subject gaze at the visual stimulus, the electroencephalography (EEG) signal of the subject is acquired and analyzed through the occipital region of the subject’s brain. The brain signal of the subject will contain the same frequency response as the selected stimulus frequency and its harmonic response. After the raw EEG signal of the subject is preprocessed and recognized by the algorithm we developed, the feature of EEG corresponding to the selected visual stimulus can be extracted and used as the input of the BCI decision model to finish recognistion and control application.
In this study, the SSVEP method was used in several BCI applications. In the SSVEP-based BCI application for spelling system, the implementation of entropy encoding algorithm with dummy character (EEA_D) can yield information transfer rate (ITR) equaling to 42.62 bits/min which is greater than the ITR of the balance structure that equals to 31.9 bits/min. In the SSVEP-based BCI application for controlling air swimmer toys, the implementation of fuzzy tracking and control algorithm as decision model yields higher recognition rate equaling to 96.97% compared to the canonical correlation analysis (CCA) as a baseline which yields 94.49%. The fuzzy feature threshold algorithm applied in SSVEP-based BCI application for controlling mobile robot can yield ITR equaling to 20.57 bits/minutes. The fuzzy decision model with fast Fourier transform (FFT) and magnitude squared coherence (MSC) input features can yield F-score equaling to 0.5889 and 0.6038 in single visual stimulus test (SVST) and multiple visual stimuli test (MVST) respectively, while the CCA baseline yielded 0.4052 and 0.4005 in SVST and MVST respectively. Based on this result, the SSVEP-based BCI using the proposed approach is successfully developed on spelling system, air swimmer toys, mobile robot, and robot with the function of meal assistance.

Table of Contents

Abstract i
中文摘要 iii
Acknowledgment iv
Table of Contents v
List of Figures vii
List of Tables viii
CHAPTER 1: INTRODUCTION 1
A. Background of the study 1
B. Motivation 5
C. Objectives and overview of the study 6
CHAPTER 2: REVIEW OF RELATED LITERATURE 7
A. Amyotrophic lateral sclerosis (ALS) & Motor Neuron Disease (MND) 7
B. Human Electroencephalography (EEG) 9
1. EEG Recording and Measurement 9
2. Electrode Positioning 9
3. Type of EEG signal 11
C. Brain Computer Interface (BCI) 12
D. Steady State Visual Evoked Potential (SSVEP)-based BCI 14
1. Methods used in SSVEP-based BCI 15
2. Applications of SSVEP-based BCI 18
CHAPTER 3: MATERIALS AND METHODS 22
A. The Structure of the Research 22
B. SSVEP-based BCI Application for Spelling System 24
1. The Collection of Dataset 25
2. Design of Multilevel Selection Interface 25
3. Feature Extraction and Decision Model 27
C. SSVEP-based BCI Application for Controlling Air Swimmer Toys 29
1. Visual Stimulus 29
2. Signal Acquisition 30
3. Decision Model 30
D. SSVEP-based BCI Application for Controlling Mobile Robot 33
1. Visual Stimulus 33
2. Signal Acquisition 34
3. Decision Model of the SSVEP-based BCI 35
4. Hardware of the Mobile Robot 38
E. SSVEP-based BCI Application for Controlling Meal Assistance Robot 41
1. The Visual Stimulation and EEG acquisition 41
2. Feature Extraction 42
3. Decision model of SSVEP-based BCI 44
4. The feeding robot 46
CHAPTER 4: RESULTS AND DISCUSSION 48
A. SSVEP-based BCI Application for Spelling System 48
1. Experimental Results of Multilevel Selection Interface 48
2. Experimental Results of SSVEP-Based BCI Text Input System 53
B. SSVEP-based BCI Application for Controlling Air Swimmer Toys 55
C. SSVEP-based BCI Application for Controlling Mobile Robot 59
1. Robot movement simulation test 59
2. Real robot control test 64
D. SSVEP-based BCI Application for Controlling Meal Assistance Robot 67
1. Single visual stimulus test (SVST) 67
2. Multiple visual stimuli test (MVST), 67
CHAPTER 5: CONCLUSION AND FUTURE WORKS 73
A. Conclusions 73
B. Future Works 75
REFERENCES 77

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