臺灣博碩士論文加值系統

大腦人機介面大多以想像左手、右手、腳與吐舌等體感動作為主，然而，以想像彩色圖像和中文文字作為大腦人機介面的媒介，目前少有相關研究團隊從事這方面研究，所以本研究在此試驗性地探討想像彩色圖像和中文文字的腦電波差異，並設計適合這種認知實驗的提示，此為本研究的主要動機和貢獻。
腦電波記錄時會包含著許多雜訊，例如:市電干擾、生理雜訊…等，而且腦電波訊號具有時變、非穩態等特性，所以腦電波的「特徵擷取」以及「分類」這兩個方面一直是主要的研究課題。本研究在「特徵擷取」方面，是採用多尺度主成分分析法來分析腦電波訊號，本分析方法分為兩個階段：第一階段是利用離散小波轉換中的多貝西小波，來分解腦電波訊號變成數個子頻帶，以增加特徵數量；第二階段是用主成分分析法，來得到最顯著的特徵值及其對應的特徵向量。本研究在「分類」方面，選擇具有高度辨識率的支持向量機，使用此來分類本研究所使用的彩色圖像與中文文字兩類想像內容。實驗受測者總共有13位，實驗結果發現以受測者S4為例，分類正確辨識率可高達88.89%，而平均正確準確率可達到72.65%。本研究更進一步探討圖像-動物類和文字-非動物類這兩種差異提示想像，實驗結果發現在圖像-動物類的平均分類正確率為74.08%，而文字-非動物類的平均分類正確率則達到84.21%，顯示對大部份受測者而言，文字-非動物類的平均分類正確率比圖像-動物類的平均分類正確率高。
本研究還有歸納出P300及N400在彩色圖像及中文文字提示的電位變化關係，N400事件相關電位在370ms~520ms間會出現一個負向的波峰，在負波峰後通常會有一個低頻的負波，這是腦電波感受到新奇刺激的反應，以及大腦空間能量分佈圖能量差異較大的頻帶位於Alpha2頻帶(11-14Hz)及Beta1頻帶(14-25Hz)，也就是常見的Beta頻帶。

Brain computer interface (BCI) is mostly lead to complete by motor imagery which includes left hand, right hand, foot, and tongue. However, less of related groups put effort to study about BCI based on imaging of color pictures and Chinese words. In this thesis, the pilot study discusses the differences of electroencephalogram (EEG) of imaging color pictures and Chinese words, and design suitable hints of cognitive tasks. The above description is main motivation and the designed hints are main contributions in this study.
EEG recordings contain various noises, for instance, the disturbance from electric supply, physiological artifacts, etc. At the same times, EEG is time-variant and non-stationary. According to these reasons, “feature extraction” and “classification” of EEG are the main issues in BCI regions. In “feature extraction”, this study adopted multi-scale principle component analysis (multi-scale PCA) to analyze EEG signals. The whole method in this study is divided into two steps: In the first step, a kind of discrete wavelet transform, Daubechies wavelet, is used to decompose EEG signals into several sub-bands and increase the number of feature. In the second step, multi-scale PCA is used to extract most distinguished eigen values and eigen vectors. In “classification”, support vector machine (SVM) which usually has high accuracy is used to classify EEG of imaging color pictures and Chinese words in this study. There are thirteen subjects jointed this experiment. The performance in the experiment, one of subjects, S4 has highest accuracy of classification which can be reach to 88.89%, and averaged accuracy of all subjects is 72.65%. The more advanced research in this study is to discuss two different hints of imagery task, “pictures - animals” and “words - non-animal”. The experimental results show that the averaged accuracy of “pictures - animals” hint is 74.08% and “words - non-animal” hint is 84.21%. This could give a conclusion that “words - non-animal” hint has better performance on the accuracy rate of classification.
Finally, this study also generalized the relationship of EEG potentials between P300 and N400 during hints of “color pictures” and “Chinese words”. The phenomena in this study revealed that event-related potential with N400 was the negative peak during 370ms-520ms. In general, the wave with low frequency after the negative peak could exist. This is the response reduced from EEG when subjects receive strange stimulations. At the same times, the most distinguished difference between EEG of imaging “color pictures” and “Chinese words” with power spectrum is located at Alpha2 band (11-14 Hz) and Beta1 band(14-25 Hz). It is also called Beta band in the common.

致謝..................................................... I
摘要.................................................... III
Abstract..................................................V
目錄.....................................................VII
圖目錄....................................................X
表目錄....................................................XV
第一章 緒論..............................................1
1.1 研究背景........................................1
1.2 腦電波.........................................6
1.2.1 腦電波的空間域分析(國際10-20系統)................7
1.2.2 腦電波的時間域分析..............................10
1.2.3 腦電波的頻率域分析..............................13
1.3 研究動機與目的.................................15
1.4 研究架構.......................................16
1.5 論文架構.......................................17
第二章 文獻探討............................................18
2.1 事件相關電位之文獻及研究概況.................... 18
2.2 事件相關電位之文獻回顧整理...................... 32
2.3 腦電波特徵擷取之文獻及研究概況.................. 34
2.4 腦電波特徵擷取之文獻回顧整理.................... 47
第三章 相關研究方法........................................49
3.1 快速傅立葉轉換（Fast Fourier Transform, FFT）...49
3.1.1 時間降次快速傅立葉轉換(DIT FFT)演算法........... 50
3.1.2 頻率降次快速傅立葉轉換(DIF FFT)演算法........... 53
3.2 數位濾波器.....................................56
3.2.1 IIR與FIR概論..................................56
3.2.2 零相位延遲濾波器................................56
3.3 小波理論.......................................59
3.3.1 傅立葉轉換 (Fourier Transform).................59
3.3.2 短時傅立葉轉換(Short-Time Fourier Transform, STFT)...60
3.3.3 小波轉換 (Wavelet Transform)...................61
3.3.3.1 小波函數(Wavelet Function).....................62
3.3.3.2 連續小波轉換(Continuous Wavelet Transform, CWT)...64
3.3.3.3 離散小波轉換(Discrete Wavelet Transform, DWT)...66
3.3.3.4 多重解析度分析(Multiresolution Analysis,MRA)...67
3.3.3.5 近似空間(Approximate spaces)與細部空間(Detail spaces)...68
3.3.3.6 Mallat 演算法.................................68
3.3.3.7 本研究使用之小波函數............................70
3.3.3.8 小波轉換與傅立葉轉換之比較......................72
3.4 多尺度主成份分析法（MSPCA）.....................74
3.5 支持向量機（Support Vector Machines , SVM）....77
第四章 實驗設計與流程......................................82
4.1 圖文提示想像實驗設計............................82
4.2 圖文提示想像實驗流程............................84
4.3 實驗環境與設備.................................86
4.3.1 實驗環境......................................87
4.3.2 實驗硬體設備與耗材.............................87
4.3.3 實驗軟體設備..................................89
4.3.4 實驗量測流程...................................90
4.4 圖文提示想像腦電波訊號前處理....................92
4.5 圖文提示想像腦電波訊號後處理...................106
4.6 圖像-動物類和文字-非動物類提示的想像腦電波訊號後處理...113
第五章 實驗結果與討論.....................................115
5.1 彩色圖像或中文文字提示引發的P300及N400事件相關電位115
5.2 大腦空間能量頻譜圖.............................119
5.3 圖像和文字提示想像的分類正確辨識率...............125
5.4 圖像-動物類和文字-非動物類提示想像的分類正確辨識率.126
5.5 各受測者的問卷統計分析.........................132
第六章 結論與建議.........................................135
6.1 結論.........................................135
6.2 建議.........................................137
參考文獻..................................................138
附錄 A.................................................. 145
附錄 B.................................................. 172
附錄 C.................................................. 192
附錄 D.................................................. 205

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