禪定組與控制組的腦電波之空間非線性相互依存源與波源特性關係之研究

Investigation of spatially nonlinear interdependence in correlation with focalized source characteristics of Zen meditation and resting EEG

楊家維(Chia-Wei Yang)

指導教授 ： 羅佩禎

國立交通大學/電機學院/電機與控制工程系所/碩士(2018年)

摘要

禪定時的腦部動態已經被引起關注且受到研究數十年，於本論文中，我們試圖探索微觀下 禪定和休息中腦電波之非線性相依性分析的振盪現象，基於相空間重建法的非線性相依性 (Nonlinear Interdependence)分析，常被應用於分析大腦神經網路之間的動態連結特性，本 論文中主要的分析包含了建立並分類微觀相似度矩陣(microstate similarity-index matrix, miSIM)，並基於微觀分析結果解釋長時間腦電波的演化現象，而本研究中，我們對於系統的狀 態提出了合適的參數並應用於建立每五毫秒三十個通道的完整 30×30 微觀相似度矩陣，此論 文主要專注在多通道腦電波非線性相依性分析的微觀現象，並藉由 Fuzzy c-means clustering 分類，此分類結果可以解釋長時間的腦電波演化現象，我們找出並利用其族群中心來做微觀分 析之分類，藉由族群中心可知道此類族群主導了長時間腦電波的現象，透過將 miSIMs 分配到 所屬於的族群，我們發現禪定腦電波出現了平衡行為互相連動神經元之間的振盪，而休息腦電 波則有擴散遠離腦中心的現象，由此可見禪定帶給我們更為全腦性的平衡與開發，並由此論文 提供了禪定對我們影響有了新的見解。

關鍵字

禪定 ； 腦電波 ； 相似值矩陣 ； 模糊分群法 ； 腦電位投影 ； 腦功能定位 ； 空間非線性相互依存關係

並列摘要

Brain dynamics in Zen-meditation state has aroused the researchers’ attention for decades. In this thesis, we attempted to explore the difference of microstate of nonlinear interdependence and the corresponding focal-source characteristics between Zen-meditation EEG (electroencephalograph) and resting EEG. Nonlinear-interdependence analysis based on phase space reconstruction may provide a feasible way to access brain dynamical interactions among regional neural networks. The analysis mainly involves the construction of similarity-index matrix (SIM), classification of microstate SIMs, and long-term EEG interpretation based on the classification results of microstate SIMs. Our previous study proposed the systematic approach for determining appropriate implementing parameters to evaluate the SI coefficient between two EEG channels and then to construct the complete 30-by-30 microstate SIM (miSIM) for the 5-millisecond, 30-channel EEG epoch. This thesis is mainly focused on1)theclassification of microstatesof spatially nonlinear interdependence of more Zen-meditation and resting EEG data, and 2) analysis of the corresponding focalized-source (dipole) characteristics based on the four-shell concentric spherical head model. The microstate SIMs are classified by Fuzzy c-means clustering. The results of
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classification can be adopted for long-term EEG interpretation. The cluster centers of all the clusters are most representative for characterizing the nonlinear brain dynamics

並列關鍵字

Zen Meditation ； EEG ； Similar Index Matrix ； Fuzzy c-means ； Brain Mapping ； Focalize source ； spatially nonlinear interdependence

參考文獻

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