This thesis is aimed to investigate the temporal evolution of alpha spatial properties of 30-channel Zen-meditation and resting EEG (electroencephalograph). Two different schemes of unsupervised classification methods are adopted in this study. The first scheme, SOM (self-organization map) is based on the matching of input feature vector (alpha brain mapping) and the weight vectors representing the quantitative features of the output neurons. The SOM is constructed by 30 input neurons (for the 30 entries extracted from alpha brain mapping) and a given number of output neurons. Accordingly, we need to determine the number of output neurons that provide appropriate clustering. In addition, to optimize the clustering result, it is necessary to carefully select the implementation parameters such as number of training steps and learning rate. From the clustering results, the features of alpha spatial property may be determined. Fuzzy c-means (FCM) is a fuzzy classifier based on the K-means. FCM algorithm differs from the K-means in the aspect that K-means is implemented with the rigid criteria. In other words, instead of reaching a crispy decision like “0/1” or “true/false”, fuzzy scheme allows the degree of truth of a statement to be between 0 and 1. Finally, we compare the performance of the clustering result between SOM and FCM. Apparently, SOM provides the clustering performance of better cohesion (inner bonding, 73.53 against 79.33) and mutual-cluster differentiation (168.72 against 143.32), yet, under the tradeoff of slight falsely-clustered rate (0.09%).
目次
Content v List of Figures vii List of Tables x Chapter 1 Introduction 1 1-1 Background and Motivation 1 1-2 Introduction of Zen meditation 5 1-3 Aims of This Study 6 1-4 Scope of thesis 7 Chapter 2 Methods and Theories 8 2-1 Introduction of EEG 8 2-2 Continuous Wavelet Transform (CWT) 11 2-3 Self-Organizing Map (SOM) 14 2-4 Fuzzy C-Means(FCM) 20 2-5 Evaluation of Clustering Performance 24 2-5-1 Cluster Center 24 2-5-2 Intra-Cluster Distance 25 2-5-3 Inter-Cluster Distance 25 2-5-4 False-Clustering 26 Chapter 3 Experiment Setup and Procedure 27 3-1 Experiment Protocol 27 3-1-1 Experimental Group 27 3-1-2 Control Group 27 3-1-3 Signal Acquisition 28 3-2 Signal Analysis 29 3-2-1 Outline of the complete scheme 29 3-2-2 Self-Organizing Map 30 3-2-3 Fuzzy C-Means 32 3-3 Parameter Analysis for SOM 33 3-3-1 Number of training step (Nts) 33 3-3-2 Neighborhood size (σ0) 36 3-3-3 Learning rate (α0) 39 Chapter 4 Results and Discussion 46 4-1 Alpha Brain Mapping Classified by SOM 46 4-1-1 Result of Zen-meditation EEG 46 4-1-2 Result of Resting EEG 60 4-1-3 Comparison between Zen-meditation EEG and Resting EEG 72 4-2 Alpha Brain Mapping Classified by FCM 77 4-2-1 Result of Zen-meditation EEG 77 4-2-2 Result of Resting EEG 81 4-3 Comparison between SOM and FCM 85 Chapter 5 Conclusion 87 5-1 Conclusion 87 5-2 Future Work 88 Reference 89
