||Evaluation of Meditation Experience Based on Artificial Intelligence Using Physiological Responses
||Department of Electrical Engineering
Support Vector Machine (SVM)
Classification and Regression Tree (CART)
Artificial Neural Network (ANN)
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。
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.
CHINESE ABSTRACT I
ENGLISH ABSTRACT III
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
PUBLICATION LIST 90
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