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

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姓名

謝宗佑(Tsung-you Hsieh) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

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
(Implementation of EEMD for Real-time control of SSVEP-actuated remote-controlled car)

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摘要(中)

許多脊髓損傷患者或是其他類型患者像是肌萎縮性側索硬化症、腦幹中風、大腦或脊髓受傷、腦性麻痺、肌肉失養症、多發性硬化症等病患。這些患者無法與外界溝通或是自由的移動，為了改善這些行動有困難的人，使他們能夠自由移動，有種使用腦部直接控制電腦機械，不用透過肌肉來控制的一個溝通與控制管道，在此稱為大腦人機介面(Brain Computer interface, BCI)，透過這個大腦人機介面，就可以讓這些脊髓損傷患者與部世界傳遞訊息以及傳遞控制命令。
本研究提出一個利用穩態視覺誘發電位達成的大腦人機介面系統，使用黃鍔所提出的整體經驗模態分解法(Ensemble Empirical Mode Decomposition, EEMD)去除基線漂移和其它雜訊，並利用Quadrature Detection來判斷SSVEP之頻率，並將此方法在LabVIEW平台上完成，之後使用433Mhz無線傳輸模組傳送控制訊號給遙控車，達到即時控制遙控車之BCI。

摘要(英)

Patients with spinal cord injury or neuromuscular disorders, such as Amyotrophic lateral sclerosis (ALS), brainstem stroke, brain or spinal cord injury, cerebral palsy, muscular dystrophies, multiple sclerosis, and etc, can not communicate with external environments. In order to solve this problem, researchers are engaging themselves in developing new techniques, which are independent of their peripheral neuromuscular functions, to help them express their intentions. One plausible way, the brain–computer interface (BCI), has drawn great attention and regarded as a potential technique.
This study adopts ensemble empirical mode decomposition (EEMD) to implement a fast steady-state visual evoked potential (SSVEP) – based BCI system. Taking the advantage of EEMD for noise suppression in pre-processing step, SSVEPs can be extracted with high signal-to-noise ratio (SNR) and it permits some phase detection technique, such as quadrature detection (QD), can be applied to estimate the existing frequency of SSVEP in a short-time data segment. The proposed system has successfully implemented to control a remote-controlled car with acceptable accuracy and high information transfer rate (ITR).

關鍵字(中)

★ 大腦人機介面
★ 穩態視覺誘發電位
★ 整體經驗模態分解法

關鍵字(英)

★ steady-state evoked potential (SSVEP)
★ brain computer interface (BCI)
★ ensemble empirical mode decomposition (EEMD)

論文目次

中文摘要 I
Abstract II
致謝 III
目錄 IV
第一章緒論 1
1.1 前言 1
1.2 研究動機 1
1.3 研究目的 2
第二章大腦結構與視覺誘發電位 3
2.1 腦部結構 3
2.1.1 大腦皮質的各功能區 3
2.1.2 腦波 4
2.1.3 腦波量測 5
2.2 視覺誘發電位 7
2.3 以穩態視覺誘發電位設計的BCI系統 10
第三章研究理論與方法 15
3.1 BCI系統架構 15
3.2 視覺刺激之設計 16
3.2.1 電腦銀幕達成閃爍光源 17
3.2.2 OpenGL 18
3.2.3 雙重緩衝 18
3.3 閃爍頻率 19
3.4 遙控車設計 19
3.4.1 車體 20
3.4.2 89C51 21
3.4.3 遙控車馬達電路 22
3.4.4 無限傳輸模組 23
3.4.5 ICL232電路 24
3.4.6 無線攝影機 25
3.4.7 無線攝影機接收器 26
3.4.8 攝影機影像顯示 27
3.5 控制面板設計 28
3.6 S型軌道設計 29
3.7腦波放大器 30
3.8 演算法流程 31
3.9 希爾伯特-黃轉換理論 32
3.9.1瞬時頻率(Instantaneous frequency) 34
3.9.2本質模態函數(IMF) 35
3.9.3經驗模態分解法 36
3.10 整體經驗模態分解法 42
3.10.1 經驗模態分解法之缺陷 42
3.10.2 模式混和 (Mode Mixing) 42
3.10.3 整體經驗模態分解法(EEMD) 44
3.10.4 EEMD拆解模擬訊號範例 48
3.10.5 EEMD拆解實際腦波訊號範例 49
3.10.6 DC直流擾動對傳統傅立葉轉換的影響 50
3.11 正交偵測 (Quadrature Detection, QD) 51
第四章實驗結果 52
4.1 實驗設計 52
4.1.1 非即時實驗設計 52
4.1.2 即時實驗設計 52
4.2 實驗結果 53
4.2.1 非即時實驗不同秒數作EEMD+QD的正確率比較 53
4.2.2 非即時實驗EEMD+QD、EMD+QD與FFT準確率比較 54
4.2.3 即時實驗遙控車S型軌道軌跡圖與每秒指令動作 55
4.2.4 即時實驗之結果統計 60
第五章結論與未來展望 61
5.1 結論 61
5.2 未來展望 62
參考文獻 63

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指導教授

李柏磊(Po-lei Lee)

審核日期

2009-7-21

推文