臺灣博碩士論文加值系統

禪定學（Meditation）是目前新興的另類輔助醫學(CAM)研究中非常重要且熱門的研究領域。禪定對於身心健康有很大幫助，尤其是關於壓力調適、血壓控制、情緒管理、防止老化、增強免疫力等許多導致慢性疾病的因素有改善，而這些均深深影響現代人健康，尤其影響政府的健康保險費用支出。另一方面禪定是一種非侵入式的活動，練習者只需要依照正確的指導，循序漸進的學習，就會有身心的改善。尤其本文以禪宗印心佛法修練者為受測對象，因為在此團體中，有許多人確實透過禪修而獲致多方面益處，諸如舒解壓力、身心健康、人格穩定、潛能開發、提昇學習與工作績效…等；對於目前台灣沉重的健保負擔及國人身心健康品質，相信得以提供一個很好的解決之道。
本論文主要貢獻在於（第一篇論文）以通訊與系統理論來了解並詮釋禪修對於身心狀況與生命特質的改變機制，並以共振理論來解說加持能量對腦電波的影響。所發表之論文中，有報告統計調查結果（第二篇論文），並且針對禪坐時的腦電波變化，發展出以小波為基礎，結合模糊分群法的DSP訊號演算詮釋法則（第三篇論文）。演算法之於禪坐腦電波變化的量化效能也充分得到驗證，並進而應用於詮釋腦電波型態與長時間禪坐腦電波劇本。
本論文中針對人體在禪定練習過程中，另外發展以小波及赫斯特指數的演算法，以便有效鑑別腦電波的beta波（第四篇論文），而beta波是禪定者感受到「內在光」（inner light）時出現的腦波。禪定者beta波出現比例愈高者，視覺誘發電位振幅同時有減少的趨勢(控制組則是振幅增長) ，顯示禪定時視覺傳導系統不易受到外界視覺刺激而變化（第五篇論文）。結合受測者口述統計、腦電波及視覺誘發電位的數據，可間接說明”內在光”存在的生理機制，這點與實際禪定練習者經驗及相關文獻所記載的「內在光」的各宗教共通經驗不謀而合。這項禪定時受測者大腦有光刺激反應的模型，是第一次相關研究中所提出的假說。

Meditation is an important topic on complementary and alternative medicine (CAM), the newly developed and fast growing research area. Meditation has significant improvement effects on health, especially on the subjects with pressure, hypertension, emotional control, anti-aging, immune system enhancement, that are critical factors on modern illness and government expenditure on health insurance. Meditation is also highly valued due to the non-invasive properties; practitioners can achieve physical and spiritual achievements by correct teaching and constantly practicing. Zen Meditation practitioners who are subjects of this research especially gain many profits from meditation practicing, they are good at moderate their emotions, stresses, and they have more stable personality, higher learning and working performances. Mediation is a very good solution to people’s health and heavy burdens of Taiwan’s health insurance budgets.
The first major contribution of this thesis is to interpret the dynamic mechanism of health and spirit under Zen meditation by communication and system theories, and explain the EEG change under blessing by circuit resonance theory. We also develop a meditation EEG interpretation principle based on wavelet features and Fuzzy C-means clustering to investigate meditation EEG types and long term meditation EEG scenarios. The entire proposed algorithm’s performances are tested by simulated and real EEG signals.
We also developed a Hurst exponent based method to identify beta EEG rhythms, that is the specific EEG patterns when practitioners feeling the “inner light” during meditation. The little variation of F-VEP amplitude during Zen meditation reflects a more stable visual perceptive system during Zen meditation that is contrary to the visual response of the control subjects under eye-closed relaxation. The subject’s narration and VEP, EEG data prevail the possibility of “inner light”, and “inner light” are match the practitioners’ experience and many religious references. The visual response model of “inner light” is the first hypothesis proposed in the academic report.

Contents
Table Index 9
Figure Index 10
Chapter 1 INTRODUCTION 12
1.1 Introduction of Zen Meditation 12
1.2 Survey of literature on the meditation research 17
Chapter 2 METHODS 19
2.1 Introduction of Biomedical signals 19
2.1.1 Introduction of EEG 19
2.1.2 Introduction of VEP 22
2.2 Data Recording Procedures 23
2.2.1 Health survey 23
2.2.2 Blessing 24
2.2.3 Meditation EEG Scenarios 27
2.2.4 VEP 28
2.3 Signal processing algorithms 29
2.3.1 Wavelet approach 29
2.3.2 Sub-Band Power 32
2.3.3 FCM clustering 33
2.3.4 FCM-merging strategies 36
2.3.5 Hurst exponent 42
2.3.6 Alpha-suppressed EEG identification 44
2.3.7 VEP waveform feature extraction 46
Chapter 3 RESULTS 49
3.1 Health Survey 49
3.1.1 Experiences of Zen practitioners 49
3.1.2 Psychological and mental health of the experimental subjects 51
3.1.3 Physiological health conditions in the experimental group 56
3.2 EEG alpha blocking during Zen meditation 62
3.3 Blessing --significant alpha blocking during blessings 64
3.4 Meditation EEG Scenarios 68
3.4.1 FCM and Wavelet 68
3.4.2 Hurst exponent 76
3.5 Correlation between VEP and alpha-suppressed EEG 81
3.5.1 Alpha-suppressed EEG 81
3.5.2 F-VEP 84
3.5.3 Feature space of EEG and F-VEP 87
Chapter 4 DISCUSSION AND CONCLUSION 89
4.1 Effect of Zen meditation on health 90
4.2 Principle of blessings 91
4.3 Meditation EEG patterns and meditation scenarios 93
4.4 Inner-light nature of Zen meditation practice 94
REFERENCES 96
Appendix-A: original survey questionnaires (Chinese version) 111

[1] Fields JZ, Walton KG, Schneider RH, Nidich S, Pomerantz R, Suchdev P, Castillo-Richmond A, Payne K, Clark ET and Rainforth M. Effect of a multimodality natural medicine program on carotid atherosclerosis in older subjects: a pilot trial of Maharishi Vedic Medicine. American Journal of Cardiology 2002; 89(8):952-958.
[2] Ghassemi J. Finding the evidence in CAM: a student's perspective. Evidence Based Complementary and Alternative Medicine 2005 Sep;2(3):395-397.
[3] Majumdar M, Grossman P, Dietz-Waschkowski B, Kersig S and Walach H. Does mindfulness meditation contribute to health? Outcome evaluation of a German sample. Journal of Alternative and Complementary Medicine 2002; 8(6):719-730.
[4] http://nccam.nih.gov/health/whatiscam/#sup1#sup1
[5] Wu Jue Miao Tian. Introduction to Zen Meditation (Chinese). Zen Cosmos Publish Ltd., Taiwan, 2004,
[6] Wu Jue Miao Tian. Zen Wisdom (Chinese). Taiwan Zen Buddhist Association, Taiwan: Taipei, 2000.
[7] Lee MS, Rim YH and Kang CW. Effects of external qi-therapy on emotions, electroencephalograms, and plasma cortisol. International Journal of Neuroscience 2004 Nov; 114(11): 1493-1502.
[8] Lu Z. Scientific qigong exploration – The wonders and mysteries of qi. Malvern, PA: Amber Leaf Press, 1997.
[9] Silva LM and Cignolini A. A medical qigong methodology for early intervention in autism spectrum disorder: a case series. American Journal of Chinese Medicine 2005; 33(2):315-327.
[10] Wallace RK. Physiological effects of transcendental meditation. Science 1970; 167:1751–1754.
[11] Bennett JE and Trinder J. Hemispheric laterality and cognitive style associated with transcendental meditation. Psychophysiology 1997; 14(3): 293-296.
[12] Hebert R and Lehmann D. Theta bursts: An EEG pattern in normal subjects practicing the transcendental meditation technique. Electroencephalography and Clinical Neurophysiology 1997;42(3): 397-405.
[13] Becker DE and Shapiro D. Physiological responses to clicks during Zen, Yoga, and TM meditation. Psychophysiology 1981; 18(6): 694-699.
[14] Stigsby B, Rodenberg JC and Moth HB. Electroencephalographic findings during mantra meditation (transcendental meditation). A controlled, quantitative study of experienced meditators. Electroencephalography and Clinical Neurophysiology 1981; 51(4): 434-442.
[15] Heide FJ. Psychophysiological responsiveness to auditory stimulation during transcendental meditation. Psychophysiology 1986; 23(1): 71-75.
[16] Jevning R, Wallace RK and Beidebach M. The physiology of meditation: a review. A wakeful hypometabolic integrated response. Neuroscience and Biobehavioral Reviews 1992; 16(3): 415-424.
[17] Istratov EN, Liubimov NN and Orlova TV. Dynamic features of the modified state of consciousness during transcendental meditation. Bulletin of Experimental Biology and Medicine 1996; 121(2): 128-130.
[18] Travis F, Olson T, Egenes T and Gupta, HK. Physiological patterns during practice of the Transcendental Meditation technique compared with patterns while reading Sanskrit and a modern language International Journal of Neuroscience 2001;109 (1-2):71-80.
[19] Travis F. Autonomic and EEG patterns distinguish transcending from other experiences during Transcendental Meditation practice. International Journal of Psychophysiology 2001; 42 (1): 1-9.
[20] Travis F, Arenander A and DuBois D. Psychological and physiological characteristics of a proposed object-referral/self-referral continuum of self-awareness. Consciousness and Cognition : an International Journal 2004 Jun;13(2):401-420.
[21] Travis F, Tecce J, Arenander A and Wallace RK. Patterns of EEG coherence, power, and contingent negative variation characterize the integration of transcendental and waking states. Biological Psychology 2002 Nov;61(3): 293-319.
[22] Travis F and Arenander A. EEG asymmetry and mindfulness meditation. Psychosomatic Medicine 2004 Jan-Feb; 66(1): 147-148.
[23] Anand BK. Yoga and medical sciences. Indian J Physiol Pharmacol 1991Apr;35(2): 84-87.
[24] Elson BD, Hauri P and Cunis D. Physiological changes in yoga meditation. Psychophysiology 1977 Jan;14(1):52-57.
[25] DiBenedetto M, Innes KE, Taylor AG, Rodeheaver PF, Boxer JA, Wright HJ and Kerrigan DC. Effect of a gentle Iyengar yoga program on gait in the elderly: an exploratory study. Archives of Physical Medicine and Rehabilitation 2005 Sep;86(9):1830-1837.
[26] Brown RP and Gerbarg PL. Sudarshan Kriya Yogic Breathing in the Treatment of Stress, Anxiety, and Depression: Part II-Clinical Applications and Guidelines. Journal of Alternative and Complementary Medicine 2005 Aug;11(4):711-717.
[27] Litscher G, Wenzel G, Niederwieser G and Schwarz G. Effects of QiGong on brain function. Neurological Research 2001; 23(5):501-505.
[28] Rapgay L, Rinpoche VL and Jessum R. Exploring the nature and functions of the mind: a Tibetan Buddhist meditative perspective. Progress in Brain Research 2000; 122:507-515.
[29] Takahashi T, Murata T, Hamada T, Omori M, Kosaka H, Kikuchi M, Yoshida H and Wada Y. Changes in EEG and autonomic nervous activity during meditation and their association with personality traits. International Journal of Psychophysiology 2005 Feb;55(2):199-207.
[30] Murata T, Takahashi T, Hamada T, Omori M, Kosaka H, Yoshida H and Wada Y. Individual trait anxiety levels characterizing the properties of zen meditation. Neuropsychobiology 2004; 50(2): 189-194.
[31] Woolfolk RL. Psychophysiological correlates of meditation. Archives of General Psychiatry 1975 Oct;32(10):1326-1333.
[32] Kubota Y, Sato W, Toichi M, Murai T, Okada T, Hayashi A and Sengoku A. Frontal midline theta rhythm is correlated with cardiac autonomic activities during the performance of an attention demanding meditation procedure. Cognitive Brain Research 2001; 11(2): 281-287.
[33] Staples JK and Gordon JS. Effectiveness of a mind-body skills training program for healthcare professionals. Alternative Therapies in Health and Medicine 2005 Jul-Aug;11(4):36-41.
[34] Barnes VA, Bauza LB and Treiber FA. Impact of stress reduction on negative school behavior in adolescents. Health and Quality of Life Outcomes 2003; 1: 10.
[35] Kjaer TW, Bertelsen C, Piccini P, Brooks D, Alving J and Lou HC. Increased dopamine tone during meditation-induced change of consciousness. Cognitive Brain Research 2002 Apr; 13(2): 255-259.
[36] King MS, Carr T and D'Cruz C. Transcendental meditation, hypertension and heart disease. Australian Family Physician 2002 Feb; 31(2):164-168.
[37] Davidson RJ, Kabat-Zinn J, Schumacher J, Rosenkranz M, Muller D, Santorelli SF, Urbanowski F, Harrington A, Bonus K and Sheridan JF. Alterations in brain and immune function produced by mindfulness meditation. Psychosomatic Medicine 2003 Jul-Aug; 65(4): 564-570.
[38] Yu T, Tsai HL and Hwang ML. Suppressing tumor progression of in vitro prostate cancer cells by emitted psychosomatic power through Zen meditation. American Journal of Chinese Medicine 2003; 31(3):499-507.
[39] Jevning R, Wallace RK and Beidebach M. The physiology of meditation: A review : A wakeful hypometabolic integrated response. Neuroscience and Biobehavioral Reviews 1992 Fall; 16(3): 415-424.
[40] Peng CK, Henry IC, Mietus JE, Hausdorff JM, Khalsa G, Benson H and Goldberger AL. Heart rate dynamics during three forms of meditation. International Journal of Cardiology 2004 May; 95(1):19-27.
[41] Cysarz D and Bussing A. Cardiorespiratory synchronization during Zen meditation. European Journal of Applied Physiology 2005 Jun 7.
[42] Banquet JP. Spectral analysis of the EEG in meditation. Electroencephalography and Clinical Neurophysiology 1973 Aug; 35(2):143-151.
[43] Aftanas LI and Golocheikine SA. Human anterior and frontal midline theta and lower alpha reflect emotionally positive state and internalized attention: high-resolution EEG investigation of meditation. Neuroscience Letters 2001 Sep; 310(1): 57-60.
[44] Aftanas LI and Golocheikine SA. Non-linear dynamic complexity of the human EEG during meditation. Neuroscience Letters 2002 Sep; 330(2):143-146.
[45] Aftanas LI and Golosheykin SA. Impact of regular meditation practice on EEG activity at rest and during evoked negative emotions. International Journal of Neuroscience 2005 Jun; 115(6): 893-909.
[46] Kim YY, Choi JM, Kim SJ, Park SK, Lee SH and Lee HK. Changes in EEG of children during brain respiration-training. American Journal of Chinese Medicine 2002; 30 (2-3): 405-417.
[47] Lutz A, Greischar LL, Rawlings NB, Ricard M and Davidson RJ. Long-term meditators self-induce high-amplitude gamma synchrony during mental practice. Proceedings of the National Academy of Sciences of the United States of America 2004 Nov; 101(46): 16369-16373.
[48] Lo PC, Huang ML and Chang KM. EEG Alpha Blocking Correlated with Perception of Inner Light During Zen Meditation. American Journal of Chinese Medicine 2003; 31(4): 629-642.
[49] West MA. Meditation and the EEG. Psychological Medicine 1980 May; 10(2):369–375.
[50] Haas LF. Hans Berger (1873-1941), Richard Caton (1842-1926), and electroencephalography. Journal of Neurology Neurosurgery and Psychiatry 2003 Jan;74(1):9
[51] Cooper R, Osselton JW and Shaw JC. EEG Technology. 3rd ed., Butterworth Inc., Woburn, MA, 1980.
[52] Smith JR. Automated analysis of sleep EEG data, Handbook of Electroencephalography and Clinical Neurophysiology, 2 (chapter 4), Lopes Da Silva FH et al. ed., Elsevier Science Publishers, 1986.
[53] Niedermeyer E and Lopes Da Silva FH. Electroencephalography: Basic Principles,Clinical Applications,and Related Fields. 4th ed., Williams & Wilkins, USA, 1999.
[54] Oohashi T, Kawai N, Honda M, Nakamura S, Morimoto M, Nishina E and Maekawa T. Electroencephalographic measurement of possession trance in the field. Clinical Neurophysiology 2002 Mar; 113(3); 435-445.
[55] De Carli F, Nobili L, Beelke M, Watanabe T, Smerieri A, Parrino L, Terzano MG and Ferrillo F. Quantitative analysis of sleep EEG microstructure in the time-frequency domain. Brain Research Bulletin 2004 Jun; 63(5): 399-405.
[56] Wahlberg P and Lantz G. Methods for robust clustering of epileptic EEG spikes, IEEE Transactions on Biomedical Engineering 2000 Jul; 47(7): 857-868.
[57] Geva AB and Kerem DH. Forecasting generalized epileptic seizures from the EEG signal by wavelet analysis and dynamic unsupervised fuzzy clustering. IEEE Transactions on Biomedical Engineering 1998 Oct; 45(10): 1205-1216.
[58] Liu HS, Zhang T and Yang FS. A multistage, multimethod approach for automatic detection and classification of epileptiform EEG IEEE Transactions on Biomedical Engineering 2002 Dec; 49(12): 1557-1566.
[59] Blanco S, D'Attellis CE, Isaacson SI, Rosso OA and Sirne RO. Time-frequency analysis of eletroencephalogram series. III. Gabor and wavelet transforms. Physical Review E 1996 Dec; 54(6): 6661-6672.
[60] Al-Nashash HA, Paul JS, Ziai WC, Hanley DF and Thakor NV. Wavelet entropy for subband segmentation of EEG during injury and recovery. Annals of Biomedical Engineering 2003 Jun; 31(6): 653-658.
[61] Gutierrez J, Alcantara R and Medina V. Analysis and localization of epileptic events using wavelet packets. Medical Engineering & Physics 2001 Nov; 23(9): 623-631.
[62] Cardenas-Barrera J, Lorenzo-Ginori J and Rodriguez-Valdivia E. A wavelet-packets based algorithm for EEG signal compression. Medical Informatics and the Internet in Medicine 2004 Mar; 29(1): 15-27.
[63] Gigola S, Ortiz F, D'Attellis CE, Silva W and Kochen S. Prediction of epileptic seizures using accumulated energy in a multiresolution framework. Journal of Neuroscience Methods 2004 Sep 30; 138(1-2):107-111.
[64] Guler I and Ubeyli ED. Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients.Journal of Neuroscience Methods 2005 Jul 27.
[65] Rosso OA, Figliola A, Creso J and Serrano E. Analysis of wavelet-filtered tonic-clonic electroencephalogram recordings. Medical & Biological Engineering & Computing 2004 Jul; 42(4): 516-523.
[66] Melek WW, Lu Z, Kapps A and Fraser WD. Comparison of trend detection algorithms in the analysis of physiological time-series data. IEEE Transactions on Biomedical Engineering 2005 Apr; 52(4): 639-651.
[67] Allen R and Smith D. Neuro-fuzzy closed-loop control of depth of anaesthesia. Artificial Intelligence in Medicine 2001 Jar-Mar; 21(1-3): 185-191.
[68] Zhang XS and Roy RJ. Derived fuzzy knowledge model for estimating the depth of anesthesia. IEEE Transactions on Biomedical Engineering 2001 Mar; 48(3):312-323.
[69] Huupponen E, Himanen SL, Varri A, Hasan J, Saastamoinen A, Lehtokangas M and Saarinen J. Fuzzy detection of EEG alpha without amplitude thresholding. Artificial Intelligence in Medicine 2002 Feb; 24(2): 133-147.
[70] Palaniappan R, Paramesran R, Nishida S and Saiwaki N. A new brain-computer interface design using fuzzy ARTMAP. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2002 Sep; 10(3): 140-148.
[71] Ben Dayan Rubin DD, Baselli G, Inbar GF and Cerutti S. An adaptive neuro-fuzzy method (ANFIS) for estimating single-trial movement-related potentials. Biological Cybernetics 2004 Aug; 91(2): 63-75.
[72] Yin H, Zeng Y, Zhang J and Pan Y. Application of adaptive noise cancellation with neural-network-based fuzzy inference system for visual evoked potentials estimation. Medical Engineering & Physics 2004 Jan; 26(1): 87-92.
[73] Bay OF and Usakli AB. Survey of fuzzy logic applications in brain-related researches. Journal of Medical Systems 2003 Apr;27(2):215-223.
[74] Acharya U R, Faust O, Kannathal N, Chua T and Laxminarayan S. Non-linear analysis of EEG signals at various sleep stages. Computer Methods and Programs in Biomedicine 2005 Oct; 80(1): 37-45.
[75] Kelly SP, Lalor EC, Finucane C, McDarby G and Reilly RB. Visual spatial attention control in an independent brain-computer interface. IEEE Transactions on Biomedical Engineering 2005 Sep; 52(9):1588-1596.
[76] Isoglu-Alkac U, Keskindemirci G and Karamursel S. Auditory on- and off-responses and alpha oscillations in the human EEG. International Journal of Neuroscience 2004 Jul; 114(7): 879-906.
[77] Mouraux A and Plaghki L. Single-trial detection of human brain responses evoked by laser activation of A delta-nociceptors using the wavelet transform of EEG epochs. Neuroscience Letters 2004 May; 361(1-3): 241-244.
[78] Strauss DJ, Delb W and Plinkert PK. Analysis and detection of binaural interaction in auditory evoked brainstem responses by time-scale representations. Computers In Biology and Medicine 2004 Sep; 34(6):461-477.
[79] Niessing J, Ebisch B, Schmidt KE, Niessing M, Singer W and Galuske RA. Hemodynamic signals correlate tightly with synchronized gamma oscillations. Science 2005 Aug 5;309(5736): 948-951.
[80] Williams P and West M. EEG responses to photic stimulation in persons experienced at meditation. Electroencephalography and Clinical Neurophysiology 1975 Nov; 39(5): 519-522.
[81] Zhang W, Zheng R, Zhang B, Yu W and Shen X. An observation on flash evoked cortical potentials and Qigong meditation. American Journal of Chinese Medicine 1993; 21(3-4):243-249.
[82] http://www.doh.gov.tw/statistic/index.htm; Statistical data from the Bureau of National Health Insurance, Department of Health, executive Yuan Taiwan, 2003.
[83] Akay M. Wavelets in biomedical engineering. Annals of Biomedical Engineering 1995 Sep-Oct; 23(5): 531–542.
[84] Wiklund U, Akay M, Morrison S and Niklasson U. Wavelet decomposition of cardiovascular signals for baroreceptor function tests in pigs. IEEE Transactions on Biomedical Engineering 2002 Jul; 49(7):651-661.
[85] Bruns A. Fourier-, Hilbert- and wavelet-based signal analysis: are they really different approaches? Journal of Neuroscience Methods 2004 Aug; 137(2): 321-332.
[86] Graimann B, Huggins JE, Levine SP and Pfurtscheller G. Toward a direct brain interface based on human subdural recordings and wavelet-packet analysis. IEEE Transactions on Biomedical Engineering 2004 Jun; 51(6):954-962.
[87] Hafner HM, Brauer K, Eichner M, Steins A, Mohrle M, Blum A and Junger M. Wavelet analysis of cutaneous blood flow in melanocytic skin lesions. Journal of Vascular Research 2005 Jan-Feb; 42(1): 38-46.
[88] Aldroubi A and Unser M: (Eds.):Wavelets in medicine and biology. CRC, Boca, Raton 1996.
[89] Special issue on wavelets and signal processing. IEEE Transactions on Signal Processing 1993 Dec; 41(12): 3213-3600.
[90] Special issue on wavelet transforms and multiresolution signal analysis. IEEE Transactions on Information Theory 1992 Mar; 38(2): 529-924.
[91] Osowski S and Linh TH. ECG beat recognition using fuzzy hybrid neural network. IEEE Transactions on Biomedical Engineering 2001 Nov; 48(11):1265-1271.
[92] Kwak KC and Pedrycz W. Face recognition using fuzzy integral and wavelet decomposition method. IEEE Transactions on System Man and Cybernetics Part B-Cybernetics 2004 Aug; 34(4): 1666-1675.
[93] Yue X, Ye D, Zheng C and Wu X. Neural networks for improved text-independent speaker identification. IEEE Engineering in Medicine and Biology Magazine 2002 Mar-Apr; 21(2):53-58.
[94] Hathaway RJ, Bezdek JC and Pal NR. Sequential Competitive Learning and the Fuzzy C-Means Clustering Algorithms. Neural Networks 1996 Jul; 9(5):787-796.
[95] Akay M and Mulder EJ. Effects of maternal alcohol intake on fractal properties in human fetal breathing dynamics. IEEE Transactions on Biomedical Engineering 1998 Sep; 45:1097–1103.
[96] Wornell GW and Oppenheim AV. Estimation of fractal signals from noisy measurements using wavelets. IEEE Transactions on Signal Processing 1992; 40:611–623.
[97] Wornell GW and Oppenheim AV. Wavelet-based representations for a class of self-similar signals with applications to fractal modulation. IEEE Transactions on Information Theory 1992; 38: 785–800.
[98] Wu Jue Miao Tian. Diamond Sutra (Chinese). Zen Cosmos Publish Ltd., Taiwan, 2004,
[99] Barlow JS. The Electroencephalogram: Its Patterns and Origins. The MIT Press, Cambridge, MA: A Bradford Book, 1993.
[100] Delorme A and Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics. Journal of Neuroscience Methods 2004 Mar; 134(1):9-21.