發展以小波為基礎的禪定腦電波詮釋方法

Abstract

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.