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研究生:陳健修
研究生(外文):Chien-hsiu Chen
論文名稱:以P300事件相關電位為基礎之腦機介面人形機器人操控應用
論文名稱(外文):Development of a P300 Event Related Potential Based Brain-computer Interface for Humanoid Robot Control Applications
指導教授:郭重顯郭重顯引用關係
指導教授(外文):Chung-hsien Kuo
口試委員:郭重顯
口試委員(外文):Chung-hsien Kuo
口試日期:2014-07-28
學位類別:碩士
校院名稱:國立臺灣科技大學
系所名稱:電機工程系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2014
畢業學年度:102
語文別:中文
論文頁數:81
中文關鍵詞:腦機介面事件誘發電位P300時間位移相關性大型人形機器人
外文關鍵詞:Brain computer interfaceevent-related potentialP300time-shift correlationadult-size humanoid robot
相關次數:
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P300視覺事件誘發電位(Event-Related Potential, ERP)是腦機介面(Brain-Computer Interface, BCI)經常使用之實現方法;由於P300不需要大量資料之訓練與建模,所以使用者無需長時間之訓練就能熟悉操作系統;因此, P300腦機介面技術已廣泛地應用於協助脊椎損傷或中風之使用者使用電子裝置。然而腦波信號之變化量較小,往往必須將信號做5~10次之疊加才能進行判讀,造成P300之腦機介面系統需要較長之判讀時間;這對於資料傳輸速度需求較低之拼字系統應用上是可行的,然而當應用於動態機器人控制上,判讀即時性是具有挑戰性的。有鑑於此,本論文提出一以時間位移相關性(Time-shift correlation)為基礎之P300判讀演算法,此演算法可解決事件誘發電位各波峰每次出現時間偏移以及振幅大小不一之問題。同時,本文以時間位移關聯因子序列為輸入,並以類神經網路進行P300信號判讀,以達到快速判讀P300誘發電位。在實作上,本研究以自製之三通道(Cz、Pz、Oz)腦波量測儀量取腦波訊號,並將上述之時間位移相關性演算法實現於微處理器,最後應用於大型人形機器人之步行控制。
本研究所提出之操作模式包含快速模式與精確模式;當使用者操作機器人於寬敞之空間,以快速模式以較快的判斷速度對機器人下達步行指令;當機器人處於較狹小之空間,則以準確度較高之精確模式下達控制指令,協助機器人於窄巷之脫困並避免過多誤判而產生碰撞。實驗結果顯示快速模式在100次實驗中之成功率達87%,平均每分鐘可產生30.3 次正確指令;其中有87次成功判讀,23次誤判,0次無法判斷。然而在精確模式之下,成功率提升為95%,誤判率為5%,但因辨識法則條件較為嚴苛,使得平均每分鐘可產生15.2 次正確指令。
P300 is an event-related potential (ERP), and it is a popular brain-computer interface for detecting visual evocation stimulus. P300-based system has the advantages of using small amount of user’s data for training and modeling. Hence, P300 is feasible for practical applications without requiring long term training. Therefore, P300 techniques have been widely applied to control electronic devices for spinal cord injury and stroke patients.
However, the electroencephalography (EEG) signal level is very small, and usually the P300 can be observed from 5 to 10 times accumulation of EEG signals. As a consequence, P300 needs a longer time for recognition. Such a phenomenon could be available for low information transfer rate systems, such as computer spelling interfaces. Nevertheless, it is quite challenge to use P300 to control a dynamic movement system, such as robots.
To improve the information transfer rate, this work proposes a time-shift correlation approach for recognizing P300 systems. This approach is capable of dealing with the wave form variations from P300 peak time and voltage ranges. Moreover, the time-shift correlation series data is collected as the input nodes of the neural network (NN), and the classification of four LED visual stimuli is selected as the output node of the NN. Practically, a three-channel EEG instrument for collecting the EEG signals from Cz, Pz and Oz electrodes. The three-channel EEG signals are further processed to extract the time-shift correlation series data. The classification is used to control an adult-size humanoid robot.
Two modes including fast-recognition mode (FM) and accuracy-recognition mode (AM) are proposed for different operation conditions. When a robot walks in a spacious area, the FM is used to control the robot with a higher information transfer rate. When a robot walks in a crowded area, the AM is used for the consideration of high accuracy of recognition to reduce the opportunity of collision. The experimental results showed that in 100 trials, the accuracy rate of FM is 87%; the average information transfer rate is 30.3 commands/ min., and there are 13 misjudgments. In addition, the accuracy rate is improved to 95% for the AM, and the average information transfer rate decreases to 15.2 commands/ min. due to strict recognition constrains.
目錄
誌謝 I
中文摘要 II
ABSTRACT III
目錄 IV
圖目錄 VII
表目錄 XI
第1章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 論文架構 3
1.4 文獻回顧 3
1.4.1 P300腦機介面 3
1.4.2 P300腦機介面之應用 5
1.4.3 文獻回顧總結 7
第2章 腦機介面設計與演算法設計 8
2.1 腦機介面 8
2.2 本研究設計之腦機介面概述 9
2.3 時間位移相關性演算法設計 13
2.4 相關性 14
2.5 P300 時間位移 17
2.6 類神經網路 22
2.6.1 類神經網路基本理論 22
2.6.2 類神經網路訓練與應用 24
2.7 演算法 25
2.7.1 程式架構 25
2.7.2 疊加模式 26
2.7.3 快速模式 26
2.7.4 精確模式 27
第3章 實驗設計 29
3.1 腦波量測電路介紹 29
3.1.1 電路流程圖 29
3.1.2 電源供應電路 30
3.1.3 儀表放大器 30
3.1.4 中間級放大器 32
3.1.5 DRL等效電路 32
3.1.6 高通濾波器 34
3.1.7 低通濾波器 36
3.1.8 60Hz帶拒濾波器 39
3.1.9 信號壓縮平移電路 40
3.1.10 準位提升與後級放大電路 41
3.1.11 完整電路與驗證 42
3.2 LED視覺誘發面板 45
3.3 整合測試 47
3.3.1 P300信號實驗設計 47
3.3.2 機器人步態模擬軟體實驗設計 48
3.3.3 大型人形機器人實驗設計 49
第4章 實驗結果與討論 51
4.1 相關性測試 51
4.2 時間位移相關性測試 54
4.3 類神經網路訓練 57
4.4 P300信號實驗結果 61
4.5 機器人步態模擬軟體實測結果 64
4.6 大型人形機器人實測結果 68
4.7 視覺疲勞與誤判之關聯 72
第5章 結論與未來研究方向 76
5.1 結論 76
5.2 未來研究方向 76
參考文獻 78
參考文獻
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