Browsing by Author "Chang, Kang-Ming"
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Item type:Item, F-VEP AND ALPHA-SUPPRESSED EEG-PHYSIOLOGICAL EVIDENCE OF INNER-LIGHT PERCEPTION DURING ZEN MEDITATION(2006-02-25) ;Chang, Kang-Ming ;Lo, Pei-Chen ;電控工程研究所Institute of Electrical and Control EngineeringThis paper firstly reports the visual response to the inner light (a form of qi) and the outside stimulus during Zen meditation. It has been reported literally that inner light perception is the common experience for prayers or Zen-meditation practitioners. In this paper, we propose a method to quantify the EEG (electroencephalograph) and F-VEP (flash visual evoked potential) that characterize this experience. Based on wavelet analysis, we derived a set of parameters that effectively signify the alpha-suppressed EEG pattern. Furthermore, this paper presents our new results of analyzing the F-VEP data, mainly the peak amplitude. The observation of little variation in 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.Item type:Item, MEDITATION EEG INTERPRETATION BASED ON NOVEL FUZZY-MERGING STRATEGIES AND WAVELET FEATURES(2005-08-25) ;Chang, Kang-Ming ;Lo, Pei-Chen ;電控工程研究所Institute of Electrical and Control EngineeringAs the advantages of meditation have been outlined literally, scientific exploration of the meditation phenomena becomes significant. Meditation EEG may provide an access to the mental states beyond normal consciousness. It is the first attempt to score the meditation course by EEG. Wavelet analysis and fuzzy c-means (FCM) are applied in the automatic interpretation algorithm. However, FCM applied straightforward to quantitative feature vectors often results in an over-trifling interpretation. As a consequence, this paper presents novel cluster-managing strategies for achieving an interpretation closer to the result of naked-eye examination. The running gray-scale chart, derived by extracting, clustering, and coding the EEG features, reveals five different meditation scenarios differing from those of the controlled subjects.