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論文名稱(中文) 基於人工智能之使用生理反應於靜坐經驗評估
論文名稱(英文) Evaluation of Meditation Experience Based on Artificial Intelligence Using Physiological Responses
校院名稱 成功大學
系所名稱(中) 電機工程學系
系所名稱(英) Department of Electrical Engineering
學年度 103
學期 1
出版年 104
研究生(中文) 李宇皓
研究生(英文) Yu-Hao Lee
學號 N28941434
學位類別 博士
語文別 英文
論文頁數 91頁
口試委員 指導教授-林志隆
口試委員-夏允中
口試委員-陳佳如
口試委員-楊順聰
口試委員-余松年
口試委員-鄭國順
口試委員-邵揮洲
口試委員-朱聖緣
口試委員-林宗志
中文關鍵字 靜坐  腦波  支持向量機  分類回歸樹  類神經網路 
英文關鍵字 Meditation  Electroencephalography (EEG)  Support Vector Machine (SVM)  Classification and Regression Tree (CART)  Artificial Neural Network (ANN) 
學科別分類
中文摘要 靜坐能促進心理健康已廣為人知。為了能增強靜坐練習的效益,以及其所引發的狀態,對靜坐經驗進行量化評估是需要的。本論文分三個階段呈現評估靜坐經驗的方式。第二章以統計資訊呈現不同靜坐經驗者的表現。第三章以透過情緒刺激的生理反應分類靜坐經驗。第四章展示了可在靜坐時分析的評估器。
首先,本論文應用統計分析,得到不同靜坐經驗者,對於視覺化情緒刺激有不同的生理反應。實驗結果顯示,在視覺化情緒刺激時,資深靜坐者低頻腦波增強,而資淺靜坐者則是高頻腦波增強。透過相關性分析,發現在視覺化情緒刺激時,資淺靜坐者從靜坐狀態轉變至非放鬆狀態,而資深靜坐者可維持在靜坐狀態。實驗結果證實,規律靜坐可以促進情緒穩定性。且依據視覺化情緒刺激時的生理反應,是辨別長時間靜坐效益的方法。
基於統計分析的結果,人工智能科技支持向量機(support vector machine)與分類回歸樹(classification and regression tree)被應用做為分類器,進行三組靜坐經驗的分類。實驗結果顯示,支持向量機有較高的分類準確性(98%)相對於分類回歸樹(79%)。且進行強健性測試時,支持向量機表現出較高的穩定性。因此,依據視覺化情緒刺激時的生理反應,支持向量機可以評估靜坐經驗。透過實驗結果也間接證實,資深靜坐者可維持在內心平靜的狀態。
至於,要進一步發展即時靜坐經驗評估,則需要考慮在靜坐時的腦α波變化。類神經網路(artificial neural network)及支持向量機,被用來建構即時靜坐經驗評估器。結果顯示,類神經網路及支持向量機有超過98%的分類準確性。實驗數據反應出,採用後傳導方式訓練的類神經網路,有很小的機會(2%)停留在局部極小值。而支持向量機的表現與特徵值的比例轉換有關,但是類神經網路的表現則不受特徵值的比例轉換影響。進行即時靜坐經驗評估時,較長的分析時間與較短的更新時間可以增強分類器的準確性。因此,以人工智能為基礎的評估系統(類神經網路及支持向量機),可以在靜坐時,即時評估使用者的靜坐經驗。
英文摘要 Meditation is used to improve psychological well-being. To enhance the efficiency of meditation practice and a meditation-induced state, it is necessary to evaluate the meditation experience using a quantitative scientific method. In this dissertation, the author reports an evaluation of meditation experience in three phases. Chapter Two compares statistical data about experienced and novice meditators. Classification of meditation experience through responses to emotional stimulation is illustrated in Chapter Three. A way to evaluate the meditation experience in real-time is described in Chapter Four.
First, statistical analysis was applied to demonstrate differences in response to emotional visual stimuli between experienced and novice meditators. The results reveal that experienced meditators showed increases in low-frequency electroencephalography (EEG) rhythms during meditation, whereas novice meditators showed increases in high-frequency EEG rhythms in response to visual stimulation. Correlational analyses show that novice meditators changed from a meditative state to a non-relaxed state when the visual stimuli were presented, whereas experienced meditators maintained the meditative state. Statistical analysis provides evidence that regular concentrative meditation can improve emotional stability, and it suggests that recording physiological responses to visual stimuli can be a good method for identifying the effects of long-term concentrative meditation.
On the basis of statistical results, artificial intelligence techniques, the support vector machine (SVM) method and the classification and regression tree (CART), were implemented to classify the three groups of meditation experiences and help validate the interaction between emotional stability and meditation experience. The results illustrate that SVM yielded a higher accuracy rate (98%) than CART (79%), and the robustness of SVM was also greater than that of CART. SVM can thus assess a meditation experience by making use of visual emotional stimulation. The results from using a data mining approach provide evidence that experienced meditators maintained calmness of mind throughout the meditation session.
To develop rapid evaluation of meditation experience, the EEG alpha responses of the participants during meditation were treated as features of the classifiers. Artificial neural networks (ANNs) and SVM were applied to evaluate the meditation experiences. Both yielded a high accuracy rate (> 98%) in classifying meditation experiences. ANNs trained by back-propagation were stuck in local minima in only 2% of cases. The performance of SVM was highly related to feature scaling, but feature scaling had no effect on the ANN results. An extensive adjusting period and a short updated time enhanced the performance of the classifiers in evaluating the meditation experience. The artificial intelligence technologies ANN and SVM can thus be effectively used to assess the meditation experience in real time.
論文目次 CHINESE ABSTRACT I
ENGLISH ABSTRACT III
ACKNOWLEDGEMENTS VI
CONTENT VII
LIST OF TABLES IX
LIST OF FIGURES XI
CHAPTER 1 INTRODUCTION
1.1 Background 1
1.2 Aim 2
1.3 Organization of Dissertation 4

CHAPTER 2 IMPROVED EMOTIONAL STABILITY IN EXPERIENCED MEDITATORS
2.1 Literature Review 6
2.2 Materials and Methods 8
2.3 Statistical Results 13
2.4 Discussion on Statistical Results 20
2.5 Summary 24

CHAPTER 3 MEDITATION EXPERIENCE CLASSIFICATION BASED ON SUPPORT VECTOR MACHINE THROUGH THE RESPONSES ON EMOTIONAL STIMULI
3.1 Literature Review 25
3.2 Method 28
3.3 Results of Classification 35
3.4 Discussion on Meditation Experience Classification 47
3.5 Summary 50

CHAPTER 4 ARTIFICIAL NEURAL NETWORK AND SUPPORT VECTOR MACHINE-BASED REAL-TIME MEDITATION EXPERIENCE EVALUATION
4.1 Literature Review 53
4.2 Method 55
4.3 Results of Real-time Meditation Experience Evaluation 64
4.4 Discussion on Real-time Meditation Experience Evaluation 75
4.5 Summary 79

CHAPTER 5 CONCLUSION AND FUTURE WORK
5.1 Conclusion 80
5.2 Future Work 81

REFERENCES 84

PUBLICATION LIST 90
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