Abstract
This paper reports distinct spatio-spectral properties of Zen-meditation EEG (electroencephalograph), compared with resting EEG, by implementing unsupervised machine learning scheme in clustering the brain mappings of centroid frequency (BMFc). Zen practitioners simultaneously concentrate on the third ventricle, hypothalamus and corpora quadrigemina touniversalize all brain neurons to construct a detached brain and gradually change the normal brain traits, leading to the process of brain-neuroplasticity. During such tri-aperture concentration, EEG exhibits prominent diffuse high-frequency oscillations. Unsupervised self-organizing map (SOM), clusters the dataset of quantitative EEG by matching the input feature vector Fc and the output cluster center through the SOM network weights. Input dataset contains brain mappings of 30 centroid frequencies extracted from CWT (continuous wavelet transform) coefficients. According to SOM clustering results, resting EEG is dominated by global low-frequency (14.4 Hz); whereas Zen-meditation EEG exhibits globally high-frequency (>16 Hz) activities throughout the entire record. Beta waves with a wide range of frequencies are often associated with active concentration. Nonetheless, clinic report discloses that benzodiazepines, medication treatment for anxiety, insomnia and panic attacks to relieve mind/body stress, often induce beta buzz. We may hypothesize that Zen-meditation practitioners attain the unique state of mindfulness concentration under optimal body-mind relaxation.
Highlights
IntroductionEven with the advanced imaging technologies in the scope of brain research, EEG (electroencephalograph) has been still favorable due to its superior temporal resolution of brain electrical activities
Even with the advanced imaging technologies in the scope of brain research, EEG has been still favorable due to its superior temporal resolution of brain electrical activities
This paper reports distinct spatio-spectral properties of Zen-meditation EEG, compared with resting EEG, by implementing unsupervised machine learning scheme in clustering the brain mappings of centroid frequency (BMFc)
Summary
Even with the advanced imaging technologies in the scope of brain research, EEG (electroencephalograph) has been still favorable due to its superior temporal resolution of brain electrical activities. The electrical activities of human brain, recorded in the form of multi-channel waveforms, have been extensively studied in order to help clinicians diagnose and treat brain dysfunctions. EEG studies have been employed in a wide scope of studies on cognitive functions [6], psychology [7], social behavior [8] and even body-mind practices including Yoga, Tai Chi, Qi Gong and meditation [9] [10]. Our previous study reported novel findings on EEG under Zen meditation, the ancient oriental practice of Zen sect aiming to attain the state of enlightenment [11] [12] [13]. This paper presents an innovative scheme, based on unsupervised deep learning, for analyzing spatio-spectral properties of resting and Zen-meditation EEG
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