完整后设资料纪录
DC 栏位语言
dc.contributor.author廖宪正en_US
dc.contributor.authorHsien-Cheng Liaoen_US
dc.contributor.author罗佩祯en_US
dc.contributor.authorPei-Chen Loen_US
dc.date.accessioned2014-12-12T02:48:13Z-
dc.date.available2014-12-12T02:48:13Z-
dc.date.issued2006en_US
dc.identifier.urihttp://140.113.39.130/cdrfb3/record/nctu/#GT008912816en_US
dc.identifier.urihttp://hdl.handle.net/11536/77136-
dc.description.abstract本论文主要利用单变数(univariate)与多变数(multivariate)之自回归模型(autoregressive model)来分析探讨禅坐脑电波时间与空间之特性。在第一章的背景简介之后,第二章提出了一个可程式化的方法Subband-AR EEG Viewer来进行脑电波的分析,该方法主要是追踪禅坐中脑电波随时间变化的频谱特性,继而可以提供禅坐脑电波的总览。为了达到这样的目的,禅坐脑电波首先会经由树状的滤波器组(filter banks)分解成子频带成份(subband components)。然后每个子频带成份再利用二阶的自回归模型求出其主要频率,利用求出来的主要频率可以针对所欲解决的问题来设计演算法。根据Subband-AR EEG Viewer,我们发展了两个特别用来研究禅坐中的视觉感知能力与禅坐脑电波时空特性的演算法。这些演算法在实际应用上不需要繁复决定参数的程序,并且因为采用了二阶自回归模型而大大的降低了运算量。因此这个方法非常适合用来进行长时间的脑电波分析与即时处理。
在禅坐的视觉诱发电位(visual evoked potentials)研究中常会遇到一个问题就是无法得知可以作为参考的实际禅坐状态来给予刺激,为了让每一个视觉诱发电位取得时的大脑状态尽量维持一致,我们选择在前额alpha波出现时给予闪光刺激,这是因为前额alpha波被发现在禅坐的过程中会有显着的增加,因此,我们根据第二章所述的Subband-AR EEG Viewer设计出一个即时的alpha波侦测器,如此一来,每一个视觉诱发电位便会是在类似的背景脑电波下所取得。然后我们再利用alpha波下之视觉诱发电位(alpha-dependent F-VEPs)来研究禅坐中大脑对于刺激的动态变化。根据实验设计所得出的结果显示出禅坐组与控制组有显着的差异,与控制组在休息状态下的比较下,禅坐组在禅坐中,特别是在Cz和Fz的视觉诱发电位P1-N2和N2-P2的振幅上有明显的增加。因此,我们推测禅坐会导致主要视觉皮质层以及其相关区域对于闪光刺激产生较大振幅的反应。
另一个由Subband-AR EEG Viewer演绎过来的演算法为一个结合多重解析度(multi-resolution)技术与自回归模型的脑电波解读器,它可以辨别出脑电波的低振幅波,delta, theta, chi, alpha和beta波,另外,对于常见的杂讯如基准线飘移(baseline drift)和肌电图干扰(electromyograph interference)也可以被这个解读器所侦测出来。这个解读器拥有高效率的计算能力以及容易以硬体实现的特性,因此非常适合用来作为长时间的脑电波解读以及即时的脑电波处理,它也可以对于大量的脑电波记录提供一个快速的总览。因此,禅坐脑电波阶段性的变化就可经由不同灰阶值表示不同的脑电波特征所构成的图表显示出来。实验结果显示了禅坐组与控制组在时间与空间的脑电波节律特征上有很大的不同,特别是禅坐中beta节律在大脑上的传递现象。
除了单变数的自回归模型外,在这论文的最后,我们也提出了残余共变异矩阵(residual covariance matrix),系根据多变数的自回归模型所发展出来的一个评估脑电波时空一致性的指标:LSTS(local spatiotemporal synchronization)指标。LSTS 指标针对大脑局部区域上相邻脑电波频道间一致性的程度进行估测。利用QR分解,LSTS 指标可以有效率的被计算出来。另外,我们也提供了自回归模型阶数与相邻频道形态选择的策略。根据初期的结果显示脑电波频道间一致性的降低(去一致性)会使得LSTS 指标的数值增加。为了评估这个指标的有效性,我们设计了一个由外部指示(externally-paced)的手指运动实验,结果显示在主要运动区所产生的去一致性成功的反应出较高数值的LSTS 指标。 因此,LSTS 指标或许可被用来研究如禅坐等尚未被完全了解的心智活动下大脑的动态变化。在我们的初步结果中,当禅坐中的低振幅波出现时,LSTS 指标显示了整体脑电波一致性的增加,而这个现象被推测为与在深层禅坐中较不被环境刺激所影响的状态有关。
zh_TW
dc.description.abstractThis dissertation reports the study on EEG (electroencephalograph) spatiotemporal characteristics under Zen meditation. Univariate and multivariate AR models were applied. Following the background introduction, Chapter 2 presents a computerized scheme Subband-AR EEG Viewer that provides a comprehensive view of the meditation EEG record. The scheme was mainly designed to trace the varying spectral characteristics in meditation EEG. To accomplish this task, a meditation EEG signal was first decomposed into subband components by tree-structured filter banks. The second-order autoregressive model was then applied to each subband component to estimate its root frequency. Based on the estimated root frequencies and sound logic, specific criterion can be deduced for a particular problem-domain application. Two algorithms were developed to investigate the visual perception under meditation and to explore the spatiotemporal characteristics of EEG rhythms. These algorithms do not require exhausting work at determining appropriate parameters in implementation. Further, due to the second-order autoregressive model adopted, the computation load is greatly reduced. This approach is practically favorable to long-term EEG monitoring and real-time processing.
In the study of evoked response potential during Zen meditation, one issue encountered was the inaccessibility to the actual meditation level or stage as a reference. By modifying Subband-AR EEG Viewer, an alternative strategy was proposed for dealing with this problem. To secure a consistent condition of the brain dynamics when applying stimulation, a scheme of recording flash visual evoked potentials (F-VEPs) was designed, with main idea of applying flash stimuli during a constant background EEG (electroencephalograph)–frontal alpha-rhythm dominating activity. This particular activity was found increasing during Zen meditation. Thus the flash-light stimulus was to be applied upon emergence of the frontal alpha-rhythm. The alpha-dependent F-VEPs were then employed to inspect the effect of Zen meditation on brain dynamics. Based on the experimental protocol proposed, considerable differences between experimental and control groups were obtained. Our results showed that amplitudes of P1-N2 and N2-P2 on Cz and Fz increased significantly during meditation, contrary to the F-VEPs of control group at rest. We thus suggest that Zen meditation results in acute response on primary visual cortex and the associated parts.
Another algorithm deduced from Subband-AR EEG Viewer was a unique interpreter that combined a multi-resolution scheme with autoregressive modeling to identify the EEG patterns including the flat wave, delta, theta, chi, alpha, and beta activities. In addition, such artifacts as the baseline drift and EMG (electromyograph) interference were identifiable in the scheme. With the merits of high computational efficiency and easy hardware realization, the method proposed is feasible for long-term EEG monitoring and online EEG processing. It also allows a quick overview of an enormous amount of EEG data and the meditation scenario can be illustrated by a running gray-scale chart with each gray tone coding a particular EEG rhythmic pattern. Moreover, results of applying the proposed scheme to an experimental group (Zen meditation practitioners) and a control group (normal, healthy subjects) revealed significant distinction in spatiotemporal characteristics of EEG rhythmic patterns, especially the spatial propagation of the beta rhythm during meditation sessions.
Besides univariate AR model, this dissertation finally presented our study on a parameter called the local spatiotemporal synchronization index (LSTS index), mainly based on residual covariance matrix of a multivariate autoregressive (mAR) model. Analysis of The LSTS index measures the degree of synchronization among neighboring channels of a local brain area. By using the QR factorization, the index can be efficiently calculated. A strategy for determining the AR model order and the array of neighboring channels was also proposed. According to preliminary results, a reduction of synchronization (or, significant desynchronization) of evaluated brain areas was quantified by a relatively high index. An externally-paced finger-movement experiment was designed to evaluate the proposed method. The LSTS index estimated successfully reflected the spatiotemporal desynchronization in the primary motor area. Accordingly, the LSTS measurement could be considered as a potential approach for investigating the spatiotemporal synchronization of unknown brain dynamics under particular mental process, such as the Zen meditation. In the preliminary findings, the LSTS index of meditation EEG revealed an increasing global synchrony for the extremely low power EEG activities (to be called the ‘flat’ waves), that had been hypothesized as a detached state of sensory perception during deep meditation.
en_US
dc.language.isoen_USen_US
dc.subject脑电波zh_TW
dc.subject自回归模型zh_TW
dc.subject禅坐zh_TW
dc.subject视觉诱发电位zh_TW
dc.subject频谱分析zh_TW
dc.subjectEEGen_US
dc.subjectAutoregressive Modelsen_US
dc.subjectMeditationen_US
dc.subjectVEPen_US
dc.subjectSpectral Analysisen_US
dc.title以自回归模型为基础之禅坐脑电波随时空变异之频谱分析zh_TW
dc.titleTime-varying Spatio-spectral Analysis of Zen-meditation EEG based on Autoregressive Modelsen_US
dc.typeThesisen_US
dc.contributor.department电控工程研究所zh_TW
显示于类别:Thesis


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