國立交通大學
電機與控制工程學系
博士論文
禪坐之腦部非線性動態研究
Towards the Brain Dynamics under Chan
Meditation Based on EEG Nonlinear
Analysis
研究生：黃瑄詠
指導教授：羅佩禎 博士
中華民國九十七年十一月
禪坐之腦部非線性動態研究
Towards the Brain Dynamics under Chan Meditation Based on
EEG Nonlinear Analysis
研 究 生：黃瑄詠 Student：Hsuan-Yung Huang
指導教授：羅佩禎 Advisor：Pei-Chen Lo
國 立 交 通 大 學
電機與控制工程學系
博士論文
A Dissertation
Submitted to Department of Electrical and Control Engineering
College of Electrical and Computer Engineering
National Chiao Tung University
In Partial Fulfillment of the Requirements
For the Degree of
Doctor of Philosophy
in
Electrical and Control Engineering
November 2008
Hsinchu, Taiwan, Republic of China
禪坐之腦部非線性動態研究
研 究 生：黃瑄詠
指導教授：羅佩禎 博士
國立交通大學 電機與控制工程研究所
摘要
儘管禪定帶給人類多方面身心的益處，然而相關的電生理理研究卻非常的 少。本篇論文以非線性動態系統的觀點，研究禪坐對於中樞神經系統的影響。主 要研究的主題是禪定腦電波訊號，同時輔以另類互補醫學儀器-ARDK 的測量。 ARDK 量測方便，因此可以就禪坐對身體健康的影響提供一個大致的輪廓。實驗 結果顯示禪坐的確可以全面促進健康狀況，尤其是在”身體能量”以及”筋骨系統” 等指數。由這些結果可推論出禪坐對身體健康有長期的影響與助益。 在禪定對腦電波的影響方面，我們基於非線性動態理論發展了兩個分析參 數：平均複雜度指標(δ )以及相似度指標(S)。此外，也使用傳統的線性參數：頻 譜分析及相關性分析，來量化禪坐對於腦電波的影響以作為比較。我們首先分析 實驗組禪定以及控制組在閉眼放鬆休息狀態，其長時間腦電波的劇本變化。藉由 快數演算法，連續δ 可以反映出腦部隨著不同的意識狀態之變化。結果顯示在深 度禪定時，腦部動態系統呈現較高的複雜度指標。另外透過連續δ ，禪坐過程中 的三種不同的階段特性也得以顯示出來。 由初步的研究結果得知，資深的禪定者比起初修者較容易進入深定，禪定品 質較好，伴隨有更顯著的身心變化。因此我們接著選取了 23 位資深的禪定者， 與 23 位無禪定經驗者做進一步的研究。我們分別對於實驗組(禪坐)與控制組(閉 眼放鬆)錄製 40 分鐘的腦電波，並選取在禪坐(放鬆)的前中後各 5 分鐘的錄製結 果做δ 以及頻譜分析。我們的實驗結果顯示，在禪坐的過程中前腦的 alpha-1(8-10Hz)以及後腦的 beta 成分的振幅較控制組增加，而閉眼放鬆的控制組則是 theta 增加。至於腦部動態參數δ 則與 beta 有高度相關，也隨著禪坐過程而增加； 至於控制組的δ 則無顯著的變化。對照禪定者的口述推測：深定狀態時伴隨著內 在光的出現，此時 beta 成分會增加，這種現象可以被δ 參數有效的反映出來。 我們的結果證實，長期禪定訓練的確可以使禪定者在禪定時引發大腦皮質層的電 訊號變化。 本論文接著以非線性多變數分析，探討禪坐時腦電波之空間交互作用的特 性。我們以相似數指標(S)量化不同錄製點間的非對稱交互影響的強度。對於 12 位資深禪定者分別錄製閉眼放鬆(R)、40 分鐘的禪定實(M)、以及 5 分鐘的守禪 心輪(Z)的腦電波。在多數大腦區域，禪坐者整體上在 M 及 Z 狀態都呈現比 R 狀 態較強的交互影響。而這種強化的趨勢尤以處於意識放空又覺知的 M 狀態更為 顯著。至於在高頻的交互影響方面 (>13Hz)，整體而言，兩種禪坐狀態都較閉眼 放鬆有較強的耦合效果。然而，這種增強的交互影響尤以處於內觀而精神統一的 Z 狀態更為顯著，並以後腦最為顯著。此外在 Z 狀態只有很少數對的電極彼此的 相互影響是非對等的。這種強度相仿的結果顯示，在禪定時不同部位的大腦皮質 彼此密集的交互作用及增強的相互影響，此特徵尤其以高頻的 Z 狀態最為顯著。 我們的實驗結果證實了隨著不同的禪定階段，引起不同的大腦動態變化，這種現 象可以以非線性指標得到良好的量化結果。
Towards the Brain Dynamics under Chan Meditation Based on
EEG Nonlinear Analysis
Student：Hsuan-Yung
Huang
Advisor：Dr. Pei-Chen Lo
Institute of Electrical and Control Engineering
National Chiao Tung University
Abstract
Orthodox Chan-Buddhist meditation brings multiform benefits to human beings but little has yet been disclosed regarding the electrophysiological characteristics of the CNS. This dissertation reports the effects of Chan meditation on CNS electrophysiological behaviors in the aspect of nonlinear brain dynamics. This work was mainly focused on the meditation electroencephalogram (EEG) signals, with the reference of a CAM instrument, ARDK. The ARDK measure might provide a feasible index for the effects of Chan meditation on the health. The results showed that Chan meditation could lead a better performance in the overall Health Condition Ratio especially in Body Energy and Musculoskeletal System. The observation allows us to infer that Chan meditation may cause a long-term effect on the practitioner to have a better health condition.
Based on the nonlinear dynamical modeling, this study developed two analyzing schemes, the averaged complexity index (δ ) and the similarity index (S), to investigate the effects and phenomena of meditation EEGs. In addition, the popular power spectral analysis and coherence evaluation were applied to meditation EEGs for comparison. EEG investigation started with the exploration of long-time records
for both experimental (n=17) and control groups (n=16) to obtain the meditation EEG schema. Using an efficient algorithm of averaged complexity index, runningδ measures may reflect how the brain dynamics switches between various states of consciousness. The results showed that brain dynamics exhibited high δ in deep meditation. Three different meditation scenarios have been identified from the running δ chart.
According to the preliminary findings of meditation scenarios, our study accordingly investigated the experienced Chan-meditation practitioners because, compared with novices, advanced practitioners might experience different physiological, cognitive, and psychological states and traits. Changes in the EEG characteristics in experienced Chan-Meditation practitioners (n = 23) during 40-minute of meditation were compared with those in the matched controls (n = 23) taking a rest for 40 minutes. δ evaluation and spectral analysis were measured in three intervals, the first-, mid- and the last-5min sessions of Chan meditation or relaxing rest, each lasting for 5 minutes. Significant increase in frontal alpha-1 (8-10Hz) and occipital beta power was found during meditation as compared with the EEG under the rest, whereas an average increase of theta power was observed in the controls. In meditation, brain dynamics exhibited high δ which correlated with more beta activity. Control subjects showed no significant change in δ level. Deeper meditation state has been reported as implications of increased beta power which might probably correlate with a particular state of consciousness involving the inner-light perception. This can be more prominent by the approach of δ estimation. Our results substantiate that long-term training with Chan-meditation does induce changes in the electro-cortical activity of the brain that are distinguishable from those observed for normal relaxation.
This dissertation further presents our study on the brain interactions of experienced Chan-meditation practitioners (n = 12) varying with the meditation stages based on the nonlinear multivariate analysis. This method of similarity index (S) was used to measure the degree of possibly asymmetric coupling among multi-recording sites. Brain interactions were compared among three phases - 40-minute meditation (M), 5-minute Chakra-focusing practice (Z) and rest with closed eyes (R). Meditators exhibited, overall, stronger interactions among multiple cortical areas in meditation stages M and Z than in the R state. This enhancement was greater in the M stage when the meditator was accompanied by a thought-free and fully consciousness state. In the high-frequency band (>13Hz), the overall interdependence was also higher in both meditation stages than at baseline rest. However, the interaction strength, especially in the posterior regions, was greatest in the Z stage, which involved internal attention. Few electrode pairs were observed with significant pair-wise asymmetry in the Z state. The similarity is a possible characteristic of dense reciprocal and strong mutual interactions between multiple cortical areas during meditation - especially in the Z state in the high-frequency band. Results of this study demonstrate that profound Chan meditation induces various dynamic states in different phases of meditation, possibly reflected by nonlinear interdependence measure.
誌 謝
我人生中的一個重要階段—博士學位求學期間，終於告一個段落了。 當論文口試結束的那一剎那，心中無限感恩，能完成這篇論文要感謝的人太 多了！首先感謝指導教授羅佩禎老師，羅老師不管是在學術上指導我，在日常生 活上老師的身教言教更令我獲益良多，她對於學術源源不絕的創意、熱情，尤其 令我敬佩！也感謝老師一路以來，在我遭遇學業與人生困境的時候，在旁支持 我、協助我。 感謝口試委員謝仁俊、蔡敦仁、許晉銓、林進燈、楊谷洋老師，老師們對於 論文的不吝指導，讓我見識到專業的深度與廣度，令我獲益匪淺。 特別感謝博士班的剛鳴、適達、權毅與憲正；剛鳴的積極帶動是我學習的典 範；最常麻煩的是適達，芝麻綠豆小事他都願意幫忙；還有權毅與憲正給予我的 協助。在這條漫長的求學路上大家相互扶持，箇中的酸甜苦澀因為有你們的同行 與砥礪，阻力化為助力，讓我得以順利的完成學業。 感謝實驗室的學長：勇哥、清泉、政勳，你們的指導與經驗，指引了我的 研究方向。感謝實驗室前前後後的成員：致豪、仕揚、威助、致亨，小波波、進 忠、啟宏、仁隆、維廷、哲賢、小逸屏、清文、政恩、昶毅、恩榮、小胖胖、Bono、 宏彥，有你們的歡樂與活力，讓我們的實驗室充滿了歡笑與光彩。 感謝室友欣瑩這幾年來的互勉與照顧，也感謝禪宗佛教會師姐師兄一路同 行，更感恩 師父開啟了我的視野，照亮我的生命。禪的世界浩瀚無邊，非常禪 愧只能用我極淺薄的知識試圖研究它，感恩有這樣的機會完成我博士學位的心 願。 最重要的是，感謝我的父母及妹妹們，父母總是在我身旁默默的支持，妹妹 們讓我在經濟、家庭上無後顧之憂，我才能自由自在的投入這條研究的道路；謝 謝你們無怨無悔的付出與耐心的等待，對我無限的疼愛與包容，只有以我微薄的 智慧與能力回報你們，我永遠愛你們。 要感謝的人太多，千言萬語，只有感恩再感恩！Contents
摘要 --- i Abstract ---iii 致謝 --- vi Contents--- vii List of Tables--- ---x List of Figures--- xi Chapter I INTRODUCTION--- 1 I-1 Backgrounds --- 1I-2 Introduction of Chan-Buddhist Meditation --- 3
I-3 Introduction of Nonlinear EEG Analysis --- 5
I-4 Aims of This Work --- --- 8
Chapter II EVALUATION OF CHAN MEDITATION BASED ON TCM PRINCIPLE - 12 II-1 Introduction of Meridian Energy: ARDK Estimation--- 13
II-2 Methods and Materials --- 18
II-3 Results --- 19
II-4 Discussion --- 25
Chapter III MATERIALS and SUBJECTS --- 28
III -1 Introduction of EEG --- 28
III -2 Spatiotemporal Characteristics of Chan Meditation EEG --- 31
III -2-1 Subjects --- 31
III -2-2 Procedures and materials --- 32
III -3-1 Subjects --- 33
III -3-2 Procedures and materials --- 34
Chapter IV METHODS --- 35
IV-1 EEG Investigation Based on Nonlinear Time Series Analyses --- 36
IV-2 Embedding: Reconstruction of System Dynamics from Observations--- 40
IV-3 Univariate Analysis: Complexity Index --- 42
III -3-1 Definition and estimation --- 43
III -3-2 Efficient method for estimation--- 43
III -3-3 Applications to meditation EEG--- 44
IV-4 Multivariate Analysis: Nonlinear Interdependence Measure --- 45
IV-4-1 Definition and estimation --- 45
IV-4-2 Asymmetric property of interdependence measure --- 47
IV-4-3 Applications to meditation EEG --- 48
IV-5 EEG Investigation Based on Spectral Power Analysis and Coherence Function Analysis --- 49
IV-5-1 Estimation of spectral power --- 50
IV-5-2 Estimation of coherence function--- 50
IV-6 Statistical Analysis --- 53
IV-6-1 Spatiotemporal characteristics of Chan meditation EEG--- 53
IV-6-2 Synchronization in different meditating phases --- 54
Chapter V RESULTS --- 55
V-1 Spatiotemporal Characteristics of Chan Meditation EEG --- 55
V-1-1 Meditation EEG features --- 55
V-1-2 Chan Meditation EEG Scenarios --- 61
V-2 Synchronization in Different Meditative Phases --- 72
V-2-1 Coherence function analysis --- 72
V-2-2 Interdependence measure analysis --- 77
Chapter VI DISCUSSION and CONCLUSION--- 87
VI-1 Spatiotemporal Characteristics of Chan Meditation EEG --- 87
VI-2 Synchronization in different meditative phases --- 92
VI-3 Conclusion --- 99
Appendix A Complexity Index --- 102
REFERENCES --- 104
List of Tables
Table 2-1 The items of Health Condition Ratio --- 17
Table 2-2 The items of System Reports --- 18
Table 2-3 Mean, standard deviation and the number of subjects whose 5 health condition parameters are within the range of normal values --- 20
Table 2-4 Mean (standard deviation) and the number of subjects whose parameters of system reports are within the range of normal values --- 24
Table 5-1 The statistical results of complexity index (δ ) and relative powers in different frequency bands of characteristic EEG features collected from 23 meditators (mean ± std for each group); implementing parameters applied: n=15, τ=5 samples, epoch length: 5 seconds (1000 samples), 20≤K≤35. --- 61 Table 5-2 Characteristics of EEGs and the range of more than 70% of δ for the control
group and three experimental groups --- 64 Table 5-3 ANOVA Results of Spectral Powers --- 69 Table 5-4 Regional mean coherence (± standard deviation) in different frequency bands
for the free meditation (M), guarding Zen Chakra (Z) and the eye-closed rest (R) sessions (A: anterior; P: posterior; P-A: posterior to anterior; A-P: anterior to posterior; R-L: right to left) --- 75 Table 5-5 Regional mean S (± standard deviation) for the M, Z and R stages (A: anterior;
P: posterior; P→A: posterior to anterior; A→P: anterior to posterior; R-L: right vs left ). * p<0.05, **p<0.01 --- 84
List of Figures
Fig. 1-1 Locations of the Zen Chakra, Dharma-Eye Chakra, and Wisdom Chakra ---- 4
Fig. 2-1 The measurement probe (left) and the measurement window (right) of ARDK --- 15
Fig. 2-2 The measurement points of ARDK (The left side is analogous to the right side) --- 15
Fig. 2-3 The meridian chart shows the energy value of 24 acupoints --- 16
Fig. 2-4 The value of Body Energy (a), Musculoskeletal System (b) and Autonomic Nervous System (c) for every subject during the two states --- 21
Fig. 2-5 Mean value of the items of health condition that showed between- and within-group changes --- 22
Fig. 2-6 The mean of System reports for both groups before and after the meditation or rest. *P <0.05, **P <0.01 --- 25
Fig. 3-1 Illustration of 10-20 system: profile view (a) and top view (b). Each region has a letter to identify the lobe location (c). Note that there exists no central lobe and the "C" letter is only used for identification purposes only --- 30
Fig. 3-2 The 30-channel electrode placement--- 30
Fig. 4-1 Block diagram illustrating the entire scheme employed in this study (a) analysis of spatiotemporal characteristics (b) synchronization of different meditating phases. --- 35
Fig. 4-2 Schematic illustration of time delay embedding. A short segment of a time series is shown in (a), and its corresponding phase space trajectory shown in
(b)--- 41
Fig. 4-3 Recording sites for ‘local’ and ‘far’ coherences (a) local anterior and posterior coherences (dotted line) (b) antero-to-posterior and postero-to-anterior directions (dotted line) --- 53
Fig. 5-1 Meditation EEG characteristics (a) The 5-sec EEG segments demonstrate six major prototypes observed during meditation (from top downwards): flat beta, high-frequency beta, alpha, alpha-1, theta and delta activities. (b) Fourier magnitude spectra of meditation EEG prototypes in (a), with baseline and low frequency components removed. (c) The 2D phase trajectories and (d) complexity indexes (δ) varying with k (number of nearest neighbors) for meditation EEG prototypes in (a). Range of k (20~35) used to estimate δ is shown by two dashed lines --- 56 Fig. 5-2 The dependence of δ on values of K when analyzing the complexity index of
the ∆ rhythmic activity. --- 59 Fig. 5-3 The 25-minute running δ charts (channel O2) for the representatives in the
control group (C1) and experimental groups (M1, M2, and M3) --- 64 Fig. 5-4 The brain mappings of δ averaged over one minute for the control group C1
and the experimental groups M1, M2, and M3. The δ mappings for the first and the last five minutes are shown. --- 65 Fig. 5-5 Brain mappings of δ ’s (leftmost column) and power spectra Pr()’s for
meditators (M, top) and control subjects (C, bottom), with EEG signals selected from the mid-5min segments of main 40min meditation/relaxation sessions---65 Fig. 5-6 Percentage of power differences between sessions derived by: (top) subtracting the first-5min Pr (θ) from the mid-5min’s; (middle) subtracting the mid-5min Pr (θ) from the last-5min’s and (bottom) subtracting the first-5min Pr (β) from the
mid-5min’s. --- 68
Fig. 5-7 The percent time during which δ > 8.3 was present within each 5-min recording period for the meditation group (M) and the control group (C)---71
Fig. 5-8 Differences of coherence (d-values) for meditation (M) and Zen chakra (Z) stages in compared with baseline rest (R ) --- 73
Fig. 5-9 Significant probability mapping showing the comparison between (a) M and R states and (b) Z and R states. It is evident that Z and M states showed significantly higher synchronization over multiple cortical areas as compared with baseline rest. --- 74
Fig. 5-10 Percentage of regional and far coherence change between stages--- 76
Fig. 5-11 Example for a 19×19 S-matrix of a 9-year meditator recorded during Z stage. For example, the leftmost bottom pixel represents S(Fp1|O2) --- 77
Fig. 5-12 A significant change of interdependence measure (P<0.01 or better) with respect to the baseline condition is mapped by a connection between the two electrodes of a pair, for the M state (a) and Z state (b). An arrow represents a sink site. For better visual clarity, intra- and inter-hemispheric connections are displayed separately. Solid line indicates an increase and dashed line indicates a decrease of S measure. --- 78
Fig. 5-13 The overall topographic differences of S (d-values) when the selected electrode was considered as a sink (left) and as a source (right) (d > 1.80, P < 0.05). The upper row shows the differences between Z state and baseline R state and the bottom row shows the changes for the M state versus R state. --- 81
Fig. 5-14 The same expressions as Fig. 5-13 but for high frequency EEG data--- 81
Fig. 5-15 The same expressions as Fig. 5-12 but for high frequency EEG data--- 82
Fig. 5-16 Percentage of regional and far interdependence change between stages --- 84 Fig. 6-1 Topographic maps of relative power (upper row of each panel) and coherence
(lower row) for different frequency bands during R, M and Z stages. Only 35 electrode pairs with the highest coherences were showed --- 95
Chapter I
INTRODUCTION
I-1 Backgrounds
As complementary and alternative medicine (CAM) became more appealing to the public in 1980’s, researchers began taking a more serious attitude toward this oriental approach for health maintenance and promotion. In 1998, National Center for Complementary and Alternative Medicine (NCCAM), one component of the National Institutes of Health within the U.S. Department of Health and Human Services, was established for promoting scientific researches on the complementary and alternative medicine. CAM is defined as a group of diverse medical and health care systems or practices that are out of the cope of conventional medicine. According to the World Health Organization, there has already been about 65% to 80% world population relying on traditional or “alternative” medicine (Elizabeth, 2003). In the United States, 36% of adults are using some form of CAM, according to the 2002 edition of the National Center for Health Statistics’ National Health Interview Survey (NHIS). Mind-body medicine, one of the five major domains of CAM, has become one of the research fields being extensively studied in medicine and clinical applications because of the growing population of users (53%) in resent years. Meditation, the main aim of this dissertation, is one of the most common mind-body interventions and is also one of the 10 most commonly used CAM therapies. (From the 2002 edition of NHIS)
For many centuries, eastern religious and secular groups, such as the Buddhists, Taoist traditionalists, and the Indian Yogis, have been practicing meditation in order to achieve certain physical, mental and spiritual ends. Meditation is a conscious mental process that induces a set of integrated physiological changes. Although diverse types of meditation
exist, most practitioners are able to experience relaxed and so-called tranquil awareness. This state has been given various descriptions such as “Satori,” “enlightenment,” “samadhi,” or “pure consciousness.”
Although individuals in the East have being practicing various forms of meditation throughout history, scientific study of meditation did not begin until it was becoming popular in the Western. With the introduction of Transcendental Meditation (TM) in the late 1960’s, meditation extended from a solely mystic process of spiritual quest and religious practice to a complementary effective method in several health situations in the last years. Scientific studies began with the focus of physiological alterations induced by the process (Bagchi & Wenger, 1957; Das & Gastaut, 1957; Anand et al., 1961; Kasamatsu & Hirai, 1966. For reviews, see Jevning et al., 1992); gradually, meditation came to deserve attentions from journals and researchers, giving place to its assessment in different indications. For several decades, scientific exploration has corroborated the effectiveness of meditation practice on the health promotion, improved cardiovascular functions and immunity, hormone-level regulation, positive emotional states, stress manipulation, anxiety reduction, depression relief, etc. (Yu et al., 2003; Newell & Sanson-Fisher, 2000; Coker, 1999; MacLean et al., 1997; Tooley et al.., 2000; Lester, 1999; Shapiro et al., 1999; Aftanas & Golocheikine, 2001; Travis & Pearson, 2000).
Among many divergent methods, the research for physical and psychological correlates of meditation has centered mostly on: Yoga from India, Transcendental Meditation (TM) in the United States, and comparatively less Buddhism of Zen meditation in Japan and Tibetan Buddhism (for a review, see Cahn & Polich, 2006). Up to the present, little has yet been disclosed regarding the phenomena of Chan meditation (Kasamatsu & Hirai, 1966; Murata et al., 1994). In the past decade, orthodox Chan-Buddhist meditation (or simply “Chan meditation”), as an unconventional therapy, has proved efficacious for many chronic diseases, infections, and even some malignant tumors. Consequently, more
people began to practice orthodox Chan-Buddhist meditation in Taiwan. It aroused our attention to the physiological investigation on the Chan-Buddhist disciples since 1998. New findings have been continuously observed and reported (Lo et al., 2003). This thesis is mainly devoted to the electroencephalograph (EEG) signals, of orthodox Chan-Buddhist practitioners.
I-2 Introduction of Chan-Buddhist Meditation
Chan meditation originating more than 2,500 years ago has been proved to benefit the health while on the way toward the ultimate Buddhahood state (Lo et al., 2003; Chang & Lo, 2005). In the preaching history of Chan-Buddhism, Buddha Shakyamuni found the eternal truth, the supreme wisdom, the noumenal energy, and the natural powers of the universe in meditation under a linden tree. The orthodox Chan Buddhism originated when Buddha Shakyamuni transmitted this light of wisdom to the Great Kashiyapa some 2,500 years ago. The same path towards perfect enlightenment (Buddhahood) was promulgated in mainland China in 527 by Bodhidharma, the 28th patriarch. The current patriarch is Chan master Wu Chueh Miao Tien (or simply “Miao Tien”), the 85th patriarch of the orthodox Chan-Buddhism Sect since the Great Kashiyapa. In orthodox Chan-Buddhist practice, very few disciples were able to catch the quintessence since it cannot be taught in any form of lectures. Written material and spoken words cannot promulgate the true message of Chan, which can only be preached by a master who has achieved the Buddhahood.
In the course of meditation, meditators experience various states of consciousness. According to their subjective narration, their mentality transcends the physiological (the fifth), mental (the sixth), subconscious (the seventh), and Alaya (the eighth) conscious state. The practice of Chan meditation itself is much more highly standardized than is a wide diversity of various Yogic meditation techniques. Chan meditation is practiced in the lotus
or half-lotus position with eye closed. Two major techniques for a beginner to get into good-quality meditation are: 1) switching the breathing habit from chest to abdominal breathing so that the breathing becomes smooth and quite, and 2) guarding some important
apertures like the Zen Chakra (inside the third ventricle), the Wisdom Chakra (corpora
quadrigemina), and the Dharma-eye Chakra (hypophysis). (refer to Introduction of Chan
Meditation, lectured by Master Wu Chueh Miao Tien, published by Zen Cosmos, 2004).
Fig. 1-1 illustrates the locations of these Chakras. Gradually, meditation releases their minds from thoughts and mental activities in both conscious and subconscious realms. The practitioners experience a state of thoughtless awareness and finally free from the interference of the subconscious. By transcending the physiological, mental, subconscious, and Alaya conscious states, the human life system is immersed in the inner energy and enters a unique status in harmony with the nature and the universe (called “the unification of heaven, earth, and human”). Through meditation, a practitioner attain the enlightened state of complete mental quiescence and subliminal consciousness tranquil.
pineal
gland
Wisdom Chakra
(corpora quadrigemina)
Dharma-Eye
Chakra
Zen Chakra
(inside the third
ventricle)
I-3 Introduction of Nonlinear EEG Analysis
In regard to an unknown system such as the brain, researchers have developed the approaches for probing the system characteristics by analyzing its output or response recordable (e.g., Galka, 2000). As in most cases multi-channel neurophysiological signals are simultaneously recorded, univariate analysis alone cannot accomplish the assessment of the interdependence among channels. Therefore, it is necessary to make use of the multivariate analysis giving more insights into the brain dynamical mechanisms. Despite their capability of approaching specific aims, univariate and multivariate time-series analysis are mostly based on the widely-used, conventional time-domain and frequency-domain approaches (see, e.g., Bendat and Piersol, 2000). Unfortunately, these methods based on linear assumption cannot give any information about the nonlinear features of the signal.
Neurons are highly nonlinear, moreover, have been demonstrated to exhibit chaotic behavior (Matsumoto and Tsuda, 1988). Due to the intrinsic nonlinearity of neuronal activities, their integrative activities constituting the brain functions are nonlinear based on a sound hypothesis. Thus, other techniques based on the nonlinear dynamic theory have been introduced and been proved useful in EEG analysis (van Gils et al., 1997). These methods have been used to quantify underlying brain dynamics and evaluate EEG spatio-temporal complexity since two decades ago (Babloyantz et al., 1985; Babloyantz and Destexhe, 1986; Lo and Principe, 1989; Rapp et al., 1989; Pijn et al., 1991; van Putten, 2001). First encouraging results claimed that EEG signals showed chaotic structure (Babloyantz et al., 1985), but further studies did not find any strong evidence of chaos in EEG (Pijn, 1990, Theiler et al., 1992 and Theiler and Rapp, 1996). In the resent years, it is accepted that EEG signals are, at least in a general sense, not (low-dimensionally) chaotic (Lehnertz et al., 2000). In spite of that, nonlinear chaotic measures are still used for a more
practical goal even if there is no sign of chaos. Invariant quantities from the representation of the signals in the phase space may reveal nonlinear structures which are inaccessible by standard linear approaches (Stam, 2005).
Methods for univariate nonlinear time series analysis were originally applied to neurophysiological data about two decades ago (Babloyantz et al., 1985). Most popular tools from nonlinear dynamical theory used for EEG analysis are dimensional computation. Complexity measure reflecting the dimensionality of underlying CNS dynamics provides a macroscopic view and thus the first measurand for quantifying an unknown system. Thereby, this parameter denotes the number of state variables required to describe the temporal dynamics of the EEG signal (Grassberger & Procaccia, 1983) and provides an index that has been roughly interpreted as a measure of the irregularity or complexity of EEG dynamics. A number of studies have reported the fruitful results of characterizing the dynamic behavior of the CNS (central nervous system) under various physiological or mental states (for recent surveys: Elbert et al., 1994; Korn & Faure, 2003; Segundo, 2001; Stam, 2005). It is well known that the dimensional complexity of the human EEG increases during various types of stimulation such as imagery (Schupp et al., 1994) and mental activity (Rapp et al., 1989; Mölle et al., 1999). Many studies also investigated the relationship between “brain complexity” and different states of consciousness. For example, correlation dimension has been useful in sleep-wake research (Pereda et al., 1998; Pradhan et al., 1995) and in studies of the depth of anesthesia (van den Broek et al., 2005; Widman et al., 2000).
Although univariate nonlinear method, such as correlation dimension, furnishes important information about the CNS characteristics, they mostly suffer from the problems of indirect estimation, computational inefficiency and bias from implementing parameters (Lo and Principe, 1989; Yaylali, 1996; Lo and Chung, 2000). To deal with the problems, we introduced the method “complexity index (δ)” (Lo and Chung, 2000; Lo and Chung, 2001)
with an efficient algorithm into long-term EEG analysis. Details of the algorithm are illustrated in Chapter IV.
In the past few years, researchers in engineering and medicine began employing several nonlinear multivariate techniques in neurophysiology. A number of studies have proposed the viewpoint of considering brain dynamics as a large ensemble of coupled nonlinear dynamical subsystems. Accordingly, significant nonlinear synchronization has been detected on the macroscopic scales of EEG channels in healthy subjects (Breakspear & Terry, 2000a, 2002b; Stam et al., 1996, 2003; Feldmann & Bhattacharya, 2004). Various types of synchronization based on nonlinear dynamical theory have been demonstrated to be the more powerful mechanism than narrow-band frequency synchronization (e.g. coherence function). Two relevant concepts are: generalized synchronization (Rulkov et al., 1995), a state in which a functional dependence between the systems exist, and phase synchronization (Rosenblum et al., 1996), a state in which the phases of the systems are correlated whereas their amplitudes may not be. In brief, these measures quantify, for a short time, the grade of predicting the state-space evolution in one system by that in the other, simultaneous system.
By analyzing the reconstructed phase space, such invariant quantities are theoretically useful in neurophysiology due to their ability to detect nonlinear interactions hidden to standard linear approaches. Nevertheless, the application of these methods to neurophysiological signals is not a plain subject. On the one hand, these signals are often noisy, non-stationary and of finite length. On the other hand, theoretical studies indicated that these indexes are not able to reveal any causal relationship among signals (Quian Quiroga et al., 2000). In this work, we go through these questions by using the modified
similarity index, a robust set of interdependence measures (Arnhold et al., 1999; Quian
Quiroga et al., 2000, 2002), for the analysis of meditation EEG. Instead of estimating predictions, similarity index quantifies how neighborhoods (i.e., recurrences) in one
attractor map into the other. This method has the advantages of sensibility to nonlinear interdependence and potential of detecting asymmetric relationships (Arnhold et al., 1999; Le van Quyen, 1998). We thus investigated the capability of this multivariate method in characterizing the nonlinear interdependence behaviors of brain dynamics under Chan meditation. Details of the algorithm are illustrated in Chapter IV.
I-4 Aims of This Work
Meditation has been used as health-enhancing techniques for centuries. Numerous studies have focused on the physiological and psychological effects of meditation, with few addressing the underlying mechanisms. Consequently, how to characterize these states of consciousness based on a scientific approach becomes a matter of significance in understanding the meditation scenario. We thus conducted a series of studies on meditation EEGs to explore the brain dynamics in the process. The meditation EEG, although it has been investigated since the 1960s, is still an open question (Anand et al.., 1961; Kasamatsu and Hirai, 1966; Wallace, 1970; Banquet, 1973; Williams and West, 1975; Woolfolk, 1975).
A number of papers have reported the EEG findings of subjects practicing various meditation techniques. Up to the present, little has yet been disclosed regarding the electrophysiological characteristics of the CNS under a particular state of consciousness⎯ orthodox Chan-Buddhist meditation. In the course of Chan meditation, meditators experience various states of consciousness. Characterization of the EEG activities in different Chan meditation states may help explore neuronal network and CNS properties involved in such “beyond-consciousness” states. One important goal of our research is to assess the nature of the system’s dynamics and its distinctive changes when brain waves make transitions between several states during the meditating process.
reference of a CAM instrument, ARDK. The ARDK measure might provide a feasible index for the effects of Chan meditation on the health. The meditation EEG analysis, on the other hand, explored possible meditation scenarios and spatial-temporal characteristics of brain dynamics revealing as electrical potential changes that correlated with different Chan meditation states.
To reliably estimate the nonlinear dynamic characteristics, intensive work is normally required for obtaining appropriate implementing parameters. The dissertation reports an alternative way of efficiently determining the parameters. Based on the nonlinear dynamical modeling of brain function, this study has developed two analyzing schemes, the averaged complexity index (δ ) and the similarity index (S), to investigate the effects and phenomena of meditation EEGs. In addition, the popular power spectral analysis and coherence evaluation were applied to meditation EEGs for comparison.
We started with the exploration of long-time meditating EEG records to obtain the meditation EEG schema. We use an efficient algorithm of averaged complexity index (Lo & Chung, 2000, 2001) that is feasible for long-term monitoring. Runningδ measures may reflect how the brain dynamics switches between various states of consciousness. Consequently, this study brings forth the time evolution of the meditating EEG scenarios.
According to the preliminary findings of meditation scenarios, we speculated that the deep meditation state was accompanied by an increase of beta activity which correlated positively with complexity. Our study accordingly employed complexity measures as well as spectral analysis in the Chan-meditation EEG analysis of spatial-temporal characteristics. Here, we mainly investigated the experienced Chan-meditation practitioners because, compared with novices, advanced practitioners might experience different physiological, cognitive, and psychological states and traits.
significant and appealing, measurement of the interdependence plays an important role in meditation EEGs. While the predominant EEG findings have indicated an increase of alpha–theta range coherence during meditation (e.g. Travis & Wallace, 1999; Dillbeck & Bronson, 1981; Orme-Johnson & Haynes, 1981), relative little is known about the nonlinear synchronization of EEG evolved with the meditation process. This dissertation further presents our study of the brain interactions varying with the meditation stages based on the nonlinear multivariate analysis. This method of “similarity index” allows the study of nonlinear interdependence among multi-recording sites and represents an alternative to the coherence function. Besides doing a 40-min Chan meditation, each practitioner was also asked to do an additional guarding Zen Chakra practice. This dissertation in the end presents our further study in characterizing the nonlinear interdependence behaviors of multichannel scalp EEG under different Chan meditation stages.
This dissertation is composed of six chapters. Chapter I introduces the background of meditation studies and data analysis based on the nonlinear dynamic theory. In addition, motivation and aim of this research are described. Chapter II reports the results and our findings from ARDK device. The statistical results of comparison between experimental subjects (meditation practitioners) and controls are presented at the end of the chapter. Chapter III is focused on the experimental setup. The beginning of this chapter discusses the experimental procedure of long-time EEG monitoring. Profiles of Chan-meditation EEG under different meditating stages are then introduced. One of the aims in this study was to explore the meditation effects on brain synchronization. Details of the signal processing methods for evaluating brain synchronization are illustrated in Chapter IV. Chapter V presents the profile of meditation EEG based on the analyses of complexity index and similarity index. The characteristic patterns of meditation EEGs will be presented with their corresponding trajectories and δ’s. In addition, various scenarios will be explored
conducted. Finally, the inter-session and inter-group differences are justified by statistical analysis. The last chapter summarizes the results of this research work and discusses possible mechanisms correlated with the findings.
Chapter II
EVALUATION OF CHAN MEDITATION BASED ON TCM
PRINCIPLE
As complementary and alternative medicine (CAM) became more appealing to the general public, researchers began taking a more serious attitude toward such empirical approaches for health maintenance and promotion developed in different cultures. Due to the therapeutic effectiveness and its holistic theorem, a number of CAM-related instruments have been devised and employed not only for medicine research but also for clinical applications. They are easily implemented and have been declared to be able to provide indexes reflecting the emotional, mental and physical health. Consequently, CAM instruments may offer an alternative to study the underlying mechanisms and activities during meditation that have not been disclosed by conventional instruments.
Energy medicine is a domain in CAM that deals with two types of energy field, physical-energy and putative-energy fields. Energy-medicine researchers propose that energy fields (also called biofields) surround and flow throughout the living beings that have not been measured by conventional instruments (Russek & Schwartz, 1996; Oschman, 2000; Hintz et al, 2003). Illness then results from disturbances of the subtle energies and imbalances in the vital energy field of the body. This vital energy or life force is known under different names in different cultures, such as qi in Traditional Chinese Medicine (TCM). Therapies involving energy fields such as meditation, acupuncture and qi gong (Sancier & Holman, 2004) accordingly treat the diseases by restoring the yin-yang balance and the flow of qi.
In this chapter, we investigate, from the viewpoint of energy medicine, phenomena of the human life system under the orthodox Chan meditation practice. Besides those derived
from conventional medical instruments (EEG, ECG, GSR etc.), we aim to employ the CAM instruments- ARDK to study the biofield characteristics of Chan meditation practitioners to gain more insight into the underlying mechanism.
II-1 Introduction of Meridian Energy: ARDK Estimation
Ryodoraku theory
The Ryodoraku theory was developed by a research group lead by Dr. Yoshio Nakatani in Japan since 1949 to 1957. They fed a current into some specific acupoints and measured the electrical value reflected to investigate the meridian electrical properties corresponding to these acupoints. Meridian points are the key to all acupuncture practice. Meridian points can be deemed as the window to a body's inner activities because they reflect the system's current functionality. They found that the measured electrical value of these acupoints on the skin will reflect the health condition which matches the TCM theory saying “disease is reflected by the twelve source acupoints”.
Instrument–ARDK
ARDK (Automatic Reflective Diagnosis System) is a meridian diagnosis system developed on the basis of integrating Ryodoraku theory, Chinese Meridian theory, Russian space research, western clinic symptoms data and statistical analysis. It was developed by the Russian Central Scientific Research Institute during the period of 1978 - 1992. This method has been tested on several thousand trials in clinical studies of which the ARDK data have been analyzed and validated. Based on the large pool of >100,000 clinical subjects in Russia and >5,000 clinical subjects in Taiwan, ARDK measurement attains an accuracy rate up to 90% now. ARDK as a fast, accurate and reliable diagnostic medium is proven to be helpful in detecting some factors that indicate the risk of disease before it
becomes symptomatic.
Recording and analyzing
The ARDK sensor gets data through skin point electrical conduction (Fig. 2-1). By applying the probe to a single representative point of a meridian, the ARDK measures each meridian’s activity level. After measuring all 24 points (12 left meridians + 12 right meridians (Fig. 2-2)), the computer program will categorize these values by using statistical methods. The subject’s meridian energy data is collected safely because the maximum detecting current fed into subject’s body by ARDK during measurement is 200µA, which is much lower than a person can feel. The measurement of one diagnostic point takes less than 5 seconds and the entire measurement process will only take about 5 minutes to complete. Numerical values of the test vary depending on the physical fitness physiological status of the subject. The device then shows the results on the computer screen. The results we will use for further analysis are as follows:
Fig. 2-2 The measurement points of ARDK. (The left side is analogous to the right side)
(1) Meridian Chart
In the Meridian Chart (Fig. 2-3), the horizontal axis denotes the 24 different meridians and the vertical axis denotes the activity level of meridians. Each symbol “+” in this chart represents one meridian. There are three different colors of horizontal dash lines on this chart. The black dash line represents the mean value for total 24 meridians, the green dash lines represent the upper and lower bound for “Best” value and the red dash lines represent the upper and lower bound for “Good” value. The value outside the red dash lines denote “too High energy” or “too Low energy” condition.
Fig. 2-3 The meridian chart shows the energy value of 24 acupoints
(2) Health Condition Ratio
It was discovered that there were certain relationships among the 24 acupoints. The ratio between acupoints correlates different health condition. When there is an illness, the ratio will be abnormal. Table 2-1 shows 5 health condition parameters and their range of normal values.
Table 2-1 The items of Health Condition Ratio
Item Health Condition Ratio Normal
Body Energy The average of the 24 acupoints. A high average indicates excess Qi and Blood while a low average indicates a deficiency of Qi and Blood.
25≤ Value ≤55
Metabolism Function
The ratio of the total of all Yin meridians divided by the total of all Yang meridians. A high ratio shows that the metabolism of the body is slower than normal while a low ratio shows that the metabolism of the body is faster than normal.
0.8≤ Ratio ≤1.2
Mental State The ratio of the total of all hands meridians divided by the total of all feet meridians. A high ratio indicates an increase in mental activities and the subject has higher mental stress with anxiety, anger and halitosis. A low ratio indicates a decrease in mental activities, the subject gets drowsy, their attention is not focused, their memory is poor, and their response time slows down, etc.
0.8≤ Ratio ≤1.2
Musculoskelet al System
The ratio of the total of all left meridians divided by the total of all right meridians. The degree of balance between the Qi and Blood in the right side and left side of body affects the function of the musculoskeletal system. When the musculoskeletal system is unbalanced, there will be obstacles. The subject will feel pain or soreness in his body. 0.8≤ Ratio ≤1.2 Autonomic Nervous System
The balance condition of the autonomic nervous system. The higher the ratio the more unbalance the autonomic nervous systems is. There are many factors that may result in an imbalance in the autonomic nervous system. Generally, this is divided into internal causes (e.g. diseases of the internal organs, endocrine disorders) and external factors (e.g. mental stresses, pressure from work, and fatigue). Based on TCM theory, this results from an imbalance of Qi and Blood in part or all of the body. An abnormality of the subject’s autonomic nerves possibly results in difficulty sleeping or pain.
Ratio ≤2
(3) System Reports
System Reports shows the condition of overall human body systems. Table 2-2 shows the 16 items of System Reports and their standard range of values. The unit of each item is percentage, the higher the value the higher the probability to get problems in that system. The last item “System Reports (SR)” is the rough value representing the overall items.
Table 2-2 The items of System Reports
Item Standard
Body energy (BE) Mental state (MS)
Autonomic Nervous System (ANS) Thyroid Gland Function (TGF) Musculoskeletal System (MS) Liver Function (LS)
Digestive System (DS) Respiratory System (RS) Endocrine System (ES) Immune System (IS) Cardiovascular System(CS) Reproductive System(Rp) Kidney Function(KF) Urinary System(US) Metabolism Function(MF) System reports(SR)
Value ≤25% : Normal ÆBest Value ≤50% : Normal ÆGood
50 < Value ≤75% : Abnormal ÆNot bad Value >75% : Subhealth(not feel good but haven’t been sick) Æ Caution
II-2 Methods and Materials
Subjects and Procedure
The experimental group included 18 Chan-meditation practitioners (13 males and 5 females; mean age 27.3±3.56 years) who had been regularly practicing Chan meditation for an average of 6.6 years (ranging from 3 to 12 years) in Taiwan Chan Buddhist Association. 17 control subjects (16 males and 1 females; mean age 26.3±5.21 years) had no experience of meditation. Experimental subjects took a 30-minute meditation, sitting in the full-lotus or half-lotus position with eyes closed. Control subjects were asked to relax their mind and body with eyes closed as possible as one can for 30 minutes. Before and after the 30-minute
meditation or rest, each subject’s meridian energy data was collected by ARDK.
Statistical analysis
Four sets of data were generated: (1) experimental subjects before meditation (M1) and (2) experimental subjects after 30 min of meditation (M2); also (3) Control group before eye-closed rest (C1) and (4) Control group after 30 min of rest (C2). Data M1 and C1 were baseline state to see if the passing of time under these conditions would create changes of the meridian energy.
In the first stage of statistical analysis we performed multiple paired Student's t-tests on the difference of Health Condition Ratios and System Reports between the two periods- before and after meditation practice in the experimental subjects and before and after rest in the control subjects. For comparison between groups (M1 vs C1, M2 vs C2), we used unpaired t-tests for pre and post recording. Only significant effects (P <0.05) involving factors are reported below.
II-3 Results
Health Condition Ratio
Table 2-3 shows the number of subjects whose 5 health condition parameters are within the range of normal values. More meditators exhibited a better performance in the overall Health Condition Ratio as compared with control subject for both pre and post recording, especially in Body Energy and Musculoskeletal System.
As showed in Fig. 2-4, the experimental subjects had a decreased value in the factors Body Energy (P <0.01) and Musculoskeletal System (P <0.05) after meditation while the control subjects have no such tendency. On the other hand, the ratio of Autonomic Nervous System increased progressively after meditation (P <0.05) while the effects of rest were not
consistent for the control subjects. The average of the items of health condition that showed significant between- and within-group change was displayed Fig. 2-5.
For comparison between groups in the baseline state, the value of Body Energy (P <0.05) and Musculoskeletal System (P <0.01) was higher in the experimental group compared with the control group, i.e. M1>C1 for these two factors (Fig. 2-5). Group comparison for Metabolism Function demonstrated a significant increase in the control group compared with the meditation group. ARDK estimates of 5 health conditions revealed no significant difference for the two groups after meditation as well as rest state
Table 2-3 Mean, standard deviation and the number of subjects whose 5 health condition parameters are within the range of normal values
Normal
subjects Body Energy
Metabolism
Function Mental State
Musculoskel etal System Autonomic Nervous System before 14 14 8 13 7 Mean± std 43.36 ± 12.23 1.07 ± 0.24 1.21 ± 0.31 0.93 ± 0.22 2.44 ± 1.26 after 13 10 10 11 7 Exp Group (18) Mean ± std 34.11 ± 17.31 1.16 ± 0.33 1.13 ± 0.37 0.82 ± 0.30 2.89 ±1.29 before 8 9 6 4 4 Mean± std 31.06 ± 18.00 1.25 ± 0.30 1.24 ± 0.47 0.72 ± 0.24 3.24 ± 1.55 after 11 7 7 7 5 Ctrl Group (17) Mean± std 28.88 ± 12.90 1.27 ± 0.30 1.22 ± 0.36 0.75 ± 0.22 2.99 ± 1.40
(a)
(b)
(c)
Fig. 2-4 The value of Body Energy (a), Musculoskeletal System (b) and Autonomic Nervous System (c) for every subject during the two states.
before after 25 55 Body Energy before after 0.8 1.2 Metabolism Function before after 0.8 1.2 Musculoskeletal System before after 0 2
Autonomic Nervous System
Fig. 2-5 Mean value of the items of health condition that showed between- and within-group changes.
System Reports
Mean, standard deviation and the number of subjects whose parameters of system reports are within the range of normal values are showed in Table 2-4. Fig. 2-6 is the mean probabilities of system reports for 18 meditators and 17 control subjects during the both states. In the system reports, the items in which more than a half of the group subjects were in the range of the normal health are Body Energy, Mental State, Autonomic Nervous System, Thyroid Gland Function and Metabolism Function. In the baseline state, experimental group showed an average of overall better health condition than the control group, especially in BE, DS, RS, IS (p<0.01), ANS, TGF, LF, ES and SR (p<0.05). After 30-min of meditation or rest, experimental group presented better health condition than the
Ctrl Exp
Control group in IS, US (p<0.01), RS, SR, CS and ES (p<0.05).
The effects of 30-min meditation resulted in a significant increase of probabilities in five items - ANS, TGF, LF, RS and SR for the experimental subjects while the effects of rest were not apparent for the control group except for an increase in CS. This tendency means to get problems in that system which is on the contrary of our supposition. The differences of other items did not reach significance in both groups because of highly individual variability.
Table 2-4 Mean (standard deviation) and the number of subjects whose parameters of system reports are within the range of normal values.
Mean(Std) and Normal subjects BE Mt ANS TGF MS LF DS RS before 16 18 16 14 9 8 4 9 Mean (std) (21.40)24.54 (16.00)16.47 (15.12)37.89 (28.89)26.50 (17.79)56.21 (23.47)52.08 (14.19)56.26 (21.86)51.50 after 14 16 14 10 7 6 3 5 Exp Group (18) Mean (std) (18.67)30.37 (23.37)16.93 (19.64)43.00 (31.03)39.09 (19.83)51.86 (14.62)60.02 (90.79)85.51 (18.32)61.06 before 9 15 10 7 8 4 3 3 Mean (std) (22.88)43.71 21.24 (22.93) 51.59 (22.78) 46.76 (34.37) 58.88 (15.78) 68.71 (18.90) 69.06 (14.37) 72.82 (20.41) after 12 17 9 8 4 2 2 2 Ctrl Group (17) Mean (std) (24.74)33.94 15.41 (15.54) 49.00 (22.86) 45.88 (37.81) 62.18 (15.56) 89.53 (94.06) 68.41 (15.20) 73.41 (16.01) Mean(Std) and Normal subjects ES IS CS Rp KF US MF SR before 5 8 6 9 5 3 18 13 Mean (std) (20.51)62.54 (28.49)48.68 (19.85)58.62 (27.51)46.66 (16.57)60.89 (14.95)66.29 (13.18)13.98 (12.16)45.38 after 1 8 6 8 2 4 18 10 Exp Group (18) Mean (std) (14.88)69.47 (18.48)50.81 (18.81)58.93 (23.99)51.34 (15.83)68.19 (12.32)63.06 14.25 (9.83) (11.95)49.57 before 1 4 5 7 6 3 9 15 Mean (std) (16.42)77.53 70.88 (21.14) 55.82 (18.70) 46.00 (36.49) 63.53 (22.51) 66.71 (18.39) 43.71 (22.88) 21.24 (22.93) after 2 4 5 7 4 0 12 17 Ctrl Group (17) Mean (std) (16.77)78.82 66.76 (18.25) 70.65 (17.95) 43.41 (31.03) 69.59 (17.87) 73.24 (11.09) 33.94 (24.74) 15.41 (15.54)
BE Mt ANS TGF MS LF DS RS ES IS CS Rp KF US MF SR 50 75 N o rm a l Ab n o rm a l Su b h e a lth Exp(before) Exp(after) Ctrl(before) Ctrl(after) BE Mt ANS TGF MS LF DS RS ES IS CS Rp KF US MF SR C1>M1 ** - * * - * ** ** * ** - - - * C2>M2 - - - - - - - * * ** * - - ** - * M2>M1 - - * * - * - * - - - * C2>C1 - - - - - - - ** - - -
Fig. 2-6 The mean of System reports for both groups before and after the meditation or rest. *P <0.05, **P <0.01
II-4 Discussion
More meditators exhibited a better performance in the overall Health Condition Ratio as compared with control subject for both pre and post recording, especially in Body Energy and Musculoskeletal System.
Body Energy is the average of the measurements from the 24 acupoints. A high average indicates excess Qi and Blood while a low average indicates a deficiency of Qi and Blood. It is evident that meditators exhibit a better performance of Body Energy than the control
subjects. This may be due to the regular practice of meditation that produces the long-term changes with greater body energy. A decline of Body Energy after meditation may be related to the deep relaxed and calm state with less energy consumed.
Metabolism Function is determined from the ratio of the total of all Yin meridians divided by the total of all Yang meridians. A high ratio for the C1 shows that the metabolism of the body for nonmeditators is slower than that for the meditators in the baseline state.
The result of Musculoskeletal System indicates the balance condition of all left meridians and all right meridians. Measurements showed that more meditators are bilateral than control subjects. In addition, meditation can cause the increased activity of the Qi and Blood in the right side of body, while the control subjects remain about unchanged before and after the rest. It is hypothesized that the imbalance of the Musculoskeletal System caused by meditation is associated with the posture (left or right lotus posture).
Finally, the ratio of Autonomic Nervous System after meditation increased after meditation. In general, meditators possess a more adaptive pattern of stress response than controls (Telles et al, 1995). Previous studies showed that autonomic activities during meditation are characterized by decreased sympathetic activity (Delmonte, 1985; Walton et al., 1995; Young & Taylor, 1998) and increased parasympathetic activity (Kubota et al., 2001; Young & Taylor, 1998). Although there are no definite connections between autonomic nervous system based on Traditional Chinese Medicine theory and on modern medicine, meditation strengthens and enhances the ability to cope with stress. Ultimately, the ratio of Mental State remains almost unchanged for both groups in the current study.
The ARDK parameters of System Reports as well as Health Condition Ratio are a general idea of the overall body system and show large individual differences. Even so, our results imply that (1) extended practice of meditation may cause a long-term effect on the
participants to have better health condition and (2) meditation exhibits greater influence on the short-term state of body system than a rest does since nonmeditators keep themselves in a state of physical stability after taking a rest.
Chapter III
MATERIALS and SUBJECTS
In this chapter, two experimental setups were introduced. The first experiment was designed to study the spatiotemporal characteristics of Chan meditation EEG. Data was collected to give a meditation EEG overview of nonlinear dynamics. Another topic of interest in the meditation study is the synchronization between separate brain regions. In section III-3, the second experiment was introduced to investigate the synchronization under different meditating phases.
III -1 Introduction of EEG
For decades, the electrical activity of the human brain (electroencephalogram, EEG) has been extensively studied in order to help clinicians diagnose and treat brain disfunctions. The German psychiatrist Hans Berger firstly recorded human brain wave (voltage fluctuations) using an amplifying machine in 1924, (Berger, 1929). The human EEG signals represent the cortical electrical activity recorded at different sites on the scalp (non-invasive recording) or on the cortex (invasive recording). The cortical potentials are the integrated excitatory (EPSP) and inhibitory postsynaptic (IPSP) potentials developed by the cell body and large dendrites of pyramidal neurons. Scalp EEG measures thus represent the accumulated activity of hundreds or thousands of neural cells near the recording electrodes (Cooper et al., 1980; Oohashi et al., 2000; Niedermeyer & Lopes Da Silva, 1999). During the past decades, advanced technologies have been continually brought forth for brain function analysis, for example, functional magnetic resonance imaging (fMRI) (Logothetis, 2001), magnetoencephalography (Cohen, 1972), position emission tonogiaphy (PET) imaging (Young et al., 1999), infrared-imaging system (Holst, 1998), etc.
Nevertheless, EEG has still been the most suitable parameter for long-term monitoring of brain functions exhibited as the lump variations of electrical activities. For instance, the effort to quantify sleep stages by EEG activities in the mid twentieth century has nourished the field of sleep study and further helped understand sleep problems, like insomnia (De Carli et al., 2004).
As normally characterized by frequency, the EEG patterns are conveniently classified into four frequency ranges: the delta (∆, f<4Hz), theta (θ, 4Hz≤f<8Hz), alpha (α, 8Hz≤f<13Hz), beta (β, 13Hz≤f≤35Hz) and gamma (γ, f>35Hz). The role of the EEG in monitoring the nervous system has been explored with regard to normal and pathological conditions. Spatiotemporal features provide an access to the detection of focal EEG phenomena and to the exploration of regional function mapping (Kalayci and Özdamar, 1995). Beside the common time-domain and frequency-domain analysis methods, we employed the nonlinear dynamic approach to quantify the time-varying EEG spectral properties.
In this work, EEG data were recorded from the scalp according to the definition of 10-20 system (see Fig. 3-1). The EEG signals were recorded by a 30-channel electrode array with a common linked-mastoid (MS1-MS2) reference. Fig. 3-2 displays the recording montage. The impedance was less than 5kΩFor each electrode.
(a) (b)
(c)
Fig. 3-1 Illustration of 10-20 system: profile view (a) and top view (b). Each region has a letter to identify the lobe location (c). Note that there exists no central lobe and the "C" letter is only used for identification purposes only.
C4 T8 FCz Fz C3 T7 CPz Pz Oz Cz FP2 F4 F8 FC4 FT8 FP1 F3 F7 FC3 FT7 O1 P3 P7 CP3 TP7 O2 P4 P8 CP4 TP8 C4 T8 FCz Fz C3 T7 CPz Pz Oz Cz FP2 F4 F8 FC4 FT8 FP1 F3 F7 FC3 FT7 O1 P3 P7 CP3 TP7 O2 P4 P8 CP4 TP8
III -2 Spatiotemporal Characteristics of Chan Meditation EEG
The preliminary study is devoted to the investigation of meditation EEG features and schema. Following experiment reports the further investigation of deep meditation state by means of selecting the practitioners with long-term practicing experiences.
III -2-1 Subjects
The EEG signals were collected from 17 Chan-Buddhist practitioners and 16 matched control subjects. Seventeen meditators (11 males and 6 females), average age 29.5 years, had been practicing Chan-Buddhist meditation for an average of 6.7 years (range 2 to 12 years) in Taiwan Chan Buddhist Association. Sixteen control subjects (11 males and 5 females), average age 27.3 years, had no experience of any forms of meditation at all.
After the preliminary study, we speculated that the deep meditation state was accompanied by an increase of beta activity which correlated positively with dimensional complexity. However, comparatively fewer studies reported the findings of beta wave and its correlation with different consciousness states. Das & Gastaut (1955) first proposed
samadhi of Kriya yogi correlating with increased amount of fast beta activity. Thereafter
yet very rarely, high-frequency beta wave or even higher frequency of gamma wave was recorded for advanced meditators capable of achieving deep meditation states or samadhi (Banquet, 1973; West, 1980; Benson et al, 1990; Benson et al., 1990; Istratov et al., 1996; Lo et al, 2003; Lehmann et al, 2001; Lutz et al, 2004). More new findings showed that EEG of well experienced Tibetan Buddhist practitioners exhibited an increase of even higher frequency of 35-44Hz gamma wave (Lehmann et al., 2001; Lutz et al., 2004). Thus, it was suggested that 1) generalized high-frequency beta rhythm characterized the deep meditation stage or 2) more advanced practitioners demonstrated an increase of fast beta activity, while unexperienced meditators demonstrated autonomic relaxation. Consequently, some modifications were performed in our further study of the Chan-meditation brain dynamics
varying with the meditation process. Since consistently substantial effects of meditation on EEGs were observed mainly in experienced meditators, we mainly investigated the practitioners with long-term practicing experiences. Additional six experienced meditators and matched controls were included for the analyses.
The experimental group now included 23 Chan-meditation practitioners (16 males and 7 females; mean age 31.5±5.7 years) who had been regularly practicing Chan meditation for an average of 8.4 years (ranging from 2 to 12 years). Normally, meditators have participated in meditation session lasted 90 min at least once a week and practiced for an average of 30-minute meditation on a daily basis. Twenty-three control subjects (15 males and 8 females; mean age 29.5±3.9 years) had no experience of meditation. Subjects in both groups were all healthy, without any reported accident or illness that might affect their EEG patterns.
III -2-2 Procedures and materials
Experimental subjects took a 40-minute meditation, sitting in the full-lotus or half-lotus position with eyes closed. Each hand formed a special mudra (called the Grand Harmony Mudra), laid on the lap of the same side. Control subjects were asked to sit and relax their mind and body with eyes closed, without falling asleep for the same period. They were asked to “not practice any mental technique but merely to sit comfortably, keep the eyes closed and think about nothing in particular, just ordinary resting”. Informed consent was obtained from all of them in written form after the experimental procedures had been fully explained. After the EEG recording, the participant completed a questionnaire with respect to relaxation, awareness and the meditation condition, i.e. the feeling, quality and depth of the meditation.
In the meditation research study, it is difficult to access changes of the consciousness state during meditation. Meditators once transcending the physiological and mental state
cannot signal the operator. One reason is that the experimental subjects frequently forget. In other words, meditators cannot attain the optimal meditation if they are obligated to follow the experimental protocol. As a consequence, quantitative results together with the post-experiment interview may provide us with a glimpse of the meditation scenario.
The EEG signals were sampled at 200 Hz after the band-pass filtering with 0.3-40Hz passband. The artifacts such as eye blinking, eyeball movement and muscle activities were manually removed by naked-eye diagnosis. The 40-min recording period of meditation/relaxation was divided into three intervals of the first 20-min, the mid 10-min and the last 10-min session. Three 5-min, artifact-free EEG epochs were extracted from each interval for further analyses. After a preliminary bandpass filtering (1.56–30 Hz), each EEG epoch was analyzed by running measurement of complexity index and power spectra with a 5-second running window and a moving step of 2.5 seconds.
III -3 Synchronization in Different Meditating Phases
One important goal is to assess the nature of the interaction between separate brain regions. Measurement of brain interdependence becomes a matter of significance in understanding the meditation EEG. This section reports the experimental setup of the investigation of meditation EEG synchronization.III -3-1 Subjects
In this study, twelve Chan-meditation practitioners (mean age: 32.5 year; range: 28– 41 years; four females; all right-handed) were investigated. Since reliable effects of meditation on the EEG activity were obtained mainly in experienced meditators, all were long-term practitioners with an average experience of 8.8 years (range 5 to 13 years) in Chan-Buddhist meditation practice. In Chan-meditation practice, one major technique of
