臺灣博碩士論文加值系統

本篇論文主要目的是提出一套分析腦電波α波空間分佈的微狀態的方法，並探討禪坐者與一般人的腦電波在微狀態中的特性及差異性。
首先利用小波分解(wavelet decomposition)和小波重建(wavelet reconstruction)將腦電波中的α波頻帶擷取出來，再以各頻帶的能量比例判斷是否為α波，並將這α波能量計算為一個30維度的能量份佈(alpha brain mapping)；接著以馬氏(manalanobis)模糊C-means(MFCM)最為分類法對各個能量分佈情形進行分類，最後選取特定的分類結果作為狀態分析。
在微狀態分析的結果中發現，在禪坐者的前額α波有較長的微狀態維持時間，根據相關的研究認為，微狀態所維持的時間與大腦中資訊處理有相當的關聯，一個 較穩定的腦部活動會有較長的微狀態維持時間，也代表在當時有較少的資訊在處理中，視為較為穩定的腦波活動狀態。

The aim of this study is to propose a method for detecting alpha wave in EEG (electroencephalograph) and analyzing the alpha spatial characteristics in a microstate aspect. We investigated and compared the brain microstates between Zen-meditation practitioners (experimental group) and non-practitioners (control group).
Firstly, EEG epochs of interest were extracted by alpha-power percentage that is at least fifty percent of total power. In the analysis, wavelet decomposition and reconstruction was adopted. Then Mahalanobis Fuzzy C-means clustering was employed in the classification scheme for various alpha mappings. Finally, the alpha-brain microstates were explored and compared for both experimental and control groups.
The preliminary results reveal a longer duration of frontal-alpha microstate observed in Zen-meditation practitioners in comparison with control subjects. From the literatures, a longer duration of microstate may imply that the brain is involved in slight information processing, reflecting a rather stabilized dynamics.

中文摘要 I
Abstract II
誌謝 III
Contents IV
List of Tables VI
List of Figures VIII

1 Introduction 1
1.1 Motivation 2
1.2 Scope of thesis 3

2 Theories and Methods 4
2.1 Outline of the scheme 7
2.2 Alpha Wave Detection 9
2.3 Mahalanobis Fuzzy C-Means (MFCM) 10
2.3.1 Mahalanobis Distance 10
2.3.2 Mahalanobis Fuzzy C-Means Algorithm 13
2.3.3 The number of clusters 16
2.4 Brain Spatial Microstates 17
2.4.1 Global Field Power (GFP) 19
2.4.2 Segmentation Method 21
2.4.3 Selection of the Window of Extreme LAP Site 23
2.5 Experimental Protocol 24

3 Experimental Results 27
3.1 Results of Brain-Mapping Classification 27
3.1.1 Control Subjects 28
3.1.2 Experimental Subjects 33
3.2 Results of Microstate Analysis 37
3.2.1 Frontal-alpha and occipital-alpha microstate in experimental group 37
3.2.2 Comparison of occipital-alpha microstates in experimental and
control groups 39
3.2.3 Results in different length of EEG 41
3.2.4 Results in frontal alpha and occipital alpha of experimental group
(only adopts maximum power location of maps) 42

4 Conclusion and Discussion 46
4.1 Summary of Current Study 46
4.2 Future Work 47

References 48

Appendixes 52
A.1 Discrete Wavelet Transform 52
A.2 Mahalanobis Distance 56

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