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姓名	葉佳龍(Chia-Lung Yeh)  查詢紙本館藏	畢業系所	電機工程學系
論文名稱	實用穩態視覺誘發電位腦機介面之設計 (Design of Practical Steady-state Visual Evoked Potential-based Brain Computer)
相關論文	★ 使用梳狀濾波器於相位編碼之穩態視覺誘發電位腦波人機介面 ★ 應用電激發光元件於穩態視覺誘發電位之腦波人機介面判斷 ★ 智慧型手機之即時生理顯示裝置研製 ★ 多頻相位編碼之閃光視覺誘發電位驅動大腦人機介面 ★ 以經驗模態分解法分析穩態視覺誘發電位之大腦人機界面 ★ 利用經驗模態分解法萃取聽覺誘發腦磁波訊號 ★ 明暗閃爍視覺誘發電位於遙控器之應用 ★ 使用整體經驗模態分解法進行穩態視覺誘發電位腦波遙控車即時控制 ★ 使用模糊理論於穩態視覺誘發之腦波人機介面判斷 ★ 利用正向模型設計空間濾波器應用於視覺誘發電位之大腦人機介面之雜訊消除 ★ 智慧型心電圖遠端監控系統 ★ 使用隱馬可夫模型於穩態視覺誘發之腦波人機介面判斷 與其腦波控制遙控車應用 ★ 使用類神經網路於肢體肌電訊號進行人體關節角度預測 ★ 使用等階集合法與影像不均勻度修正於手指靜脈血管影像切割 ★ 應用小波編碼於多通道生理訊號傳輸 ★ 結合高斯混合模型與最大期望值方法於相位編碼視覺腦波人機介面之目標偵測
檔案	[Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   [檢視]  [下載] 本電子論文使用權限為同意立即開放。 已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。 請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。
摘要(中)	腦機介面為癱瘓患者與外在環境溝通的新興技術，近幾年所發展之各式腦機介面中，穩態視覺誘發電位腦機介面因具有使用者只需簡易訓練、較佳的資訊轉換率與準確率以及低成本等特性，成為發展的重點，然而為了達到穩態視覺誘發電位較高之訊雜比，基於其振幅與頻率的特性採用視覺誘發電位閃光頻率常低於20赫茲。但是若閃光閃爍頻率低於臨界閃光頻率，反而易造成使用者視覺上的不適；此外腦電波訊號為生理狀態(例如：情緒、專注力…等)神經活動的電生理反應，所以每個人所測得腦電波是有差異的，因此需要發展一個有效的分類方法改善相關問題。本篇論文將針對上述兩個問題提出改善方法：(1)設計高工作週期的視覺誘發電位閃光頻率去減少使用者視覺上的不適，在實驗設計中，使用13.16赫茲閃光頻率的發光二極體，其每一週期（T）為76毫秒（包含了亮狀態TON與暗狀態TOFF，且T = TON + TOFF），工作週期定義為TON/T，並測試不同工作週期(從10.5% 到89.5%)之穩態視覺誘發電位與採用問卷方式去調查使用者對閃光的舒適度，由六位受測者實機控制滑鼠得到高資訊轉換率（25.08位元/分）的實驗結果可歸納89.5% 工作週期的閃光頻率對使用者具有較高舒適性亦可適用於相位編碼的穩態視覺誘發電位腦機介面。(2)設計以支持向量機為基礎的分類方法進行相位編碼穩態視覺誘發電位腦機介面之目標偵測去改善使用者之間腦電波訊號差異所造成誤判問題，首先在分類器的訓練階段，每一使用者以離線紀錄所獲得穩態視覺誘發電位的振幅與相位去訓練支持向量機分類器，隨後應用於實機應用測試上，其方法首先以Kolmogorov-Smirnov(K-S)測試穩態視覺誘發電位的相位分布是否為有效資料，若判定為無效則再加入下一個狀態的相位，直到確定為有效資料，然後將有效資料所包含的相位與其對應的振幅當作分類器的輸入用以目標偵測，實機測試所得結果的準確性高達89.88 ± 4.76 %，反應時間為1.13 ± 0.02秒與較佳的資訊轉換率（50.91 ± 8.70位元/分），可顯著改善目標偵測的準確性。
摘要(英)	Brain computer interface (BCI) is an emerging technology for paralyzed patients to communicate with external environments. Among current BCIs, the steady-state visual evoked potential (SSVEP)-based BCI has drawn great attention due to its characteristics of easy preparation, high information transfer rate (ITR), high accuracy, and low cost. Due to the amplitude-frequency characteristic of SSVEP, the flickering frequency of an SSVEP-based BCI is typically lower than 20 Hz to achieve high signal-to-noise ratio (SNR). However, a visual flicker with a flashing frequency below the critical flicker-fusion frequency often makes subjects feel flicker jerky, and causes visual discomfort. In addition, electroencephalogram (EEG) signals are electrophysiological responses reflecting the underlying neural activities which are dependent upon subject’s physiological states (e.g., emotion, attention, etc.) and usually variant among different individuals. The development of classification approaches to account for each individual’s difference in SSVEP is needed but was seldom reported. To overcome the above two problems, hence, the dissertation is divided into two studies. In the first study, we present a novel technique using high duty-cycle visual flicker to decrease user’s visual discomfort. The proposed design uses light emitting diodes (LEDs) flashing at 13.16 Hz, driven by flickering sequences consisting of repetitive stimulus cycles with a duration of T (T = 76 ms). Each stimulus cycle included an ON state with a duration TON and an OFF state with a duration TOFF (T = TON + TOFF), and the duty cycle, defined as TON/T, varied from 10.5% to 89.5%. This study also includes a questionnaire survey, and analyzes the SSVEPs induced by different duty-cycle flickers. An 89.5% duty-cycle flicker, reported as a comfortable flicker, was adopted in a phase-tagged SSVEP system. Six subjects were asked to sequentially input a sequence of cursor commands with 25.08 bits/min ITR. In the second study, a multiclass support vector machine (SVM)-based classification approach is proposed for gaze-target detections in a phase-tagged SSVEP-based BCI. In the training steps, the amplitude and phase features of SSVEP from off-line recordings were used to train a multiclass SVM for each subject. In the on-line application study, effective epochs which contained sufficient SSVEP information of gaze targets were first determined using Kolmogorov-Smirnov (K-S) test, and the amplitude and phase features of effective epochs were subsequently inputted to the multiclass SVM to recognize user’s gaze targets. The on-line performance using the proposed approach has achieved high accuracy (89.88 ± 4.76 %), fast responding time (effective epoch length = 1.13 ± 0.02 s), and the ITR was 50.91 ± 8.70 bits/min. The multiclass SVM-based classification approach has been successfully implemented to improve the classification accuracy in a phase-tagged SSVEP-based BCI. The present study has shown the multiclass SVM can be effectively adapted to each subject’s SSVEPs to discriminate SSVEP phase information from gazing at different gazed targets.
關鍵字(中)	★ 腦機介面 ★ 穩態視覺誘發電位 ★ 腦電波 ★ 支持向量機	關鍵字(英)	★ Brain computer interface (BCI) ★ electroencephalography (EEG) ★ steady-state visual evoked potential (SSVEP) ★ support vector machine
論文目次	摘要 I Abstract II 誌謝 IV Content V List of Figures VIII List of Tables X List of Abbreviations XI Chapter 1 Introduction 1 1.1 Review of Previous Work and Motivation 1 1.2 Motivation 2 1.3 Objectives of Dissertation 4 1.4 Organization of Dissertation 5 Chapter 2 Brain Rhythms and Visual Evoked Potentials 7 2.1 The Electrical Activities of the Cerebral Cortex 7 2.2 Human Brain Rhythms 7 2.3 Measurements 8 2.4 Cerebral Cortex Evoked Potentials 11 2.5 The Challenge of Visual Evoked Potential 12 Chapter 3 Extraction of single-trial cortical beta oscillatory activities in EEG signals using empirical mode decomposition (EMD) 17 3.1 Motivation and Main Tasks 17 3.2 Background 18 3.3 EMD-based Method 21 3.3.1 Subjects and Experiments 21 3.3.2 Data Analysis 22 3.4 Results and Discussion 32 3.5 Summary 38 Chapter 4 An SSVEP – Based BCI Using High Duty-Cycle Visual Flicker 41 4.1 The Design of Visual Stimulation 41 4.2 Questionnaire Survey 44 4.3 EEG Recordings and Tasks 45 4.4 Signal Processing in the Flicker Investigation Study 46 4.5 Results 50 4.5.1 Questionnaire Survey 50 4.5.2 SSVEPs in the Flicker Investigation Study 52 4.6 Discussion and Summary 55 Chapter 5 Improvement of Classification Accuracy in a Phase-tagged SSVEP-based BCI Using Multiclass SVM 60 5.1 EEG Preparation and Subjects 60 5.2 System Architecture and Visual Stimulus 61 5.3 Experimental Tasks 64 5.4 SSVEP Signal Processing 65 5.5 Gazed-target detections in the BCI Application Study 70 5.6 Designing a SVM-based Approach 72 5.7 Conclusion and Summary 78 Chapter 6 Conclusion and Future Works 82 6.1 Conclusion 82 6.2 Future Works 83 Reference 86 Publication List 102 Appendix A 103
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指導教授	李柏磊(Po-Lei Lee)	審核日期	2013-7-19
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