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姓名	張香治(Hsiang-Chih Chang)  查詢紙本館藏	畢業系所	電機工程學系
論文名稱	自動校正穩態視覺誘發電位大腦人機介面的開發 (Development of self-calibrated steady-state visual evoked potential based brain computer interface)
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)	腦電波(electroencephalography, EEG)量測大腦皮質的電流，其優勢為前置作業簡單、可攜式、低成本和高時間解析度。然而，腦電波很容易受到雜訊的影響，包括自發性腦波律動、生理雜訊和外部干擾。腦電波也會隨著使用者的生理狀態而改變，導致大腦人機介面(brain computer interface, BCI)必須經常校正以達成高準確率。因此，萃取事件相關的訊號特徵與腦波的自適性校正為腦波訊號處理上相當重要的議題。為了解決這些問題，本研究發展整體經驗模態分解(ensemble empirical mode decomposition, EEMD)為基礎的方法提取出腦波訊號，以提高視覺腦波訊號的訊雜比，並研發雙相位刺激與步進延遲閃爍序列實現自動校正穩態視覺誘發電位(steady-state visual evoked potential, SSVEP)為基礎的大腦人機介面。整體經驗模態分解法不需要預設訊號基底，具有分析非平穩時間序列的優點，可以適用於穩態視覺誘發電位的萃取應用。整體經驗模態分解法將視覺腦波訊號分解為許多的內建震盪函數，視覺腦波可以藉由選擇適當的內建震盪函數(intrinsic oscillatory function, IOF)進行重建，濾除不必要的雜訊。在雙相位刺激技術部分，每一段閃光被分為參考相位與偏移相位段落，每一個閃爍光源使用不同的偏移相位進行編碼，因此使用者的腦波可以根據參考相位與偏移相位的相位差進行校正。為了進一步提升自動校正穩態視覺誘發大腦人機介面的效率，我們進一步開發步進延遲閃爍序列(stepping delay flickering sequence, SDFS)技術，藉由給予每一個閃光選項不同的步進時間延遲，產生不同選項閃光序列之間的獨立性，使得受試者所注視的閃光選項可以藉由簡單的平均技術就能辨識出來。本研究成果，在離線與線上實驗都有高的準確率與資訊傳輸率(information transfer rate, ITR)，所完成的自動校正穩態視覺誘發大腦人機介面，目前已經可以達成97.385.97%的準確率、命令傳輸間隔(command transfer interval, CTI)為3.560.68秒的平均指令時間、以及每分鐘42.4611.47位元的資訊傳輸率。本論文開發全世界第一套具有自動校正功能的穩態視覺誘發大腦人機介面，論文的研究成果將有助於幫助漸凍人、肢體殘障病人、腦創傷病人、癱瘓病人與外界的溝通控制應用。
摘要(英)	Electroencephalography (EEG) noninvasively measures neural electrophysiological activities in human brain. It has the advantages of easy preparation, portability, low cost, and high temporal resolution. However, EEG is highly susceptible to noise, including spontaneous brain rhythm, electrophysiological artifacts and external interference. The EEG signals also can be affected by the change of subject’s physiological states. Both of these reasons causes a brain computer interface (BCI) usually has to be calibrated after using a certain time period to make sure its high accuracy. Therefore, an effective method for brain signal extraction and a self-calibrated procedure are two crucial issues to develop a practical BCI. In this dissertation, we adopted ensemble empirical mode decomposition (EEMD) to extract steady-state visual evoked potential (SSVEP). The EEMD doesn’t require pre-defined basis or statistical assumption for signal extraction which is beneficial to extract non-stationary signals. EEG signals recorded from Oz position were decomposed by EEMD into a series of intrinsic oscillatory functions (IOF). The SSVEPs were then reconstructed by selecting pertinent IOFs for noise suppression. Regarding the development of self-calibrated SSVEP-based BCI, two techniques, one is biphasic stimulation and the other is stepping delay flickering sequence (SDFS), were developed. The biphasic stimulation technique divides flickering sequences into reference phase segments and shift phase segments. By designating predefined phases to reference phase segments and shift phase segments, the phase difference between reference phase segment and phase shift segment was calculated. Since the designed phase difference between the two segments was known, subject’s SSVEP phase drifts can be corrected by comparing the measured phase difference and the designed phase difference. The second self-calibrated SSVEP-based BCI was implemented by SDFS method. By designing different SDFS with distinct stepping delay, the independency among distinct flickering sequences was achieved which resulted in subject’s gazed target can be recognized through a simple averaging process. The experimental results of this dissertation have shown high accuracy and high information transfer rate (ITR) in both off-line and on-line experiments. The averaged accuracy, command transfer interval (CTI) and ITR are 97.385.97%, 3.560.68 s and 42.4611.47 bits/min, respectively. The study result of this dissertation has achieved the first self-calibrated SSVEP-based BCI in the world. The achievement provides an effective way for amyotrophic lateral sclerosis (ALS), brain trauma and paralyzed patients to communicate with external environments.
關鍵字(中)	★ 大腦人機介面 ★ 雙相位刺激 ★ 整體經驗模態分解 ★ 腦電波 ★ 步進延遲閃爍序列 ★ 穩態視覺誘發電位	關鍵字(英)	★ Brain computer interface (BCI) ★ biphasic stimulation ★ ensemble empirical mode decomposition (EEMD) ★ electroencephalography (EEG) ★ stepping delay flickering sequence (SDFS) ★ steady-state visual evoked potential (SSVEP)
論文目次	摘要 I Abstract II 致謝 IV Contents V List of Figures VII List of Tables IX List of Abbreviations X Chapter 1 Introduction 1 1.1 Background and Motivation 1 1.2 Review of Previous Work 2 1.3 Objectives of Dissertation 3 1.4 Organization of Dissertation 5 Chapter 2 SSVEP-Based Brain Computer Interface 6 2.1 Implement a High-ITR SSVEP-based BCI system 6 2.1.1 Empirical Mode Decomposition 7 2.1.2 Ensemble Empirical Mode Decomposition 9 2.1.3 Computer Stimulation of the Capability of EEMD in Noise Reduction 11 2.2 Practical BCI System Based on SSVEP 13 2.2.1 Frequency-coded Flickering Sequence 14 2.2.2 Phase-tagged Flickering Sequence 15 2.2.3 Biphasic Stimulation 18 2.2.4 Stepping Delay Flickering Sequence (SDFS) 20 2.3 Performance Evaluation 23 Chapter 3 Brain-Wave-Actuated Small Robot Car using EEMD based approach 24 3.1 System Architecture of the Proposed SSVEP-Actuated Small Robot Car 24 3.2 Subjects and Tasks 26 3.3 Frequency Recognition in SSVEP-based BCI Using EEMD and MFD 28 3.4 Results 30 3.5 Discussion and Conclusions 36 Chapter 4 Accounting for Phase Drift in an SSVEP-Based BCI System by Means of Biphasic Stimulation 39 4.1 System Architecture of the Proposed BCI Using Biphasic Stimulation 39 4.2 Subjects and Tasks 41 4.3 Data Process 41 4.3.1 Extraction of Frequency Components 41 4.3.2 Hotelling’s t-Square Test 44 4.3.3 Rejection of Noisy Data Segments and Validation of Gaze Detections 44 4.3.4 Identification of Gazed Targets by fvalid and the Phase Difference of Valid Vectors 45 4.4 Results 47 4.5 Discussion and Conclusions 49 Chapter 5 Independence of Amplitude-Frequency and Phase Calibrations in an SSVEP-Based BCI Using SDFS 53 5.1 System Architecture of the Proposed BCI Using SDFS 53 5.2 Subjects and Tasks 55 5.3 Signal Processing of SSVEP and Detection of the Gazed Target 56 5.4 Determination of MNP Threshold from Non-gaze Condition 57 5.5 Results 58 5.6 Discussion and Conclusions 62 Chapter 6 Conclusions and Future Work 66 6.1 Conclusions 66 6.2 Future Work 67 References 69 Publications 76
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指導教授	李柏磊(Po-Lei Lee)	審核日期	2013-4-29
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