Investigation of spatial characteristics of meditation eeg using wavelet analysis and fuzzy classifier

Chuan Yi Liu*, Pei-Chen Lo

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

2 Scopus citations

Abstract

The aim of this paper is to report our preliminary results of investigating the α spatial properties in Zen-meditation EEG (electroencephalograph). Results of practitioners (experimental group) were compared with that of non-practitioners (control group). We firstly applied wavelet transform to decomposing multi-channel EEG signals and reconstructing various EEG rhythms using wavelet coefficients. From the power ratio, we selected the candidates (normalized α-power vectors) for further spatial analysis. Fuzzy C-means based algorithm was applied to the normalized vectors to explore various brain spatial characteristics during meditation (or, at rest). Here we evaluated correlation coefficients to decide the number of clusters. From the results we found (1) during meditation, the possessing ration of α power in the frontal area of meditators increased more than that of the control subjects (during relaxation with eyes closed). Contrarily, in the parietal area the possessing ratio is decreased in the experimental group but increased in the control group. (2) The ratio of non-α waves in the control group decreased dramatically during relaxation but not in the experimental group. From the literatures, activating medial prefrontal cortex and anterior cingulated cortex during meditation may be the reason of increasing frontal α power.

Original languageEnglish
Pages91-96
Number of pages6
StatePublished - 1 Dec 2007
Event5th IASTED International Conference on Biomedical Engineering, BioMED 2007 - Innsbruck, Austria
Duration: 14 Feb 200716 Feb 2007

Conference

Conference5th IASTED International Conference on Biomedical Engineering, BioMED 2007
CountryAustria
CityInnsbruck
Period14/02/0716/02/07

Keywords

  • EEG
  • Fuzzy
  • Meditation
  • Scalp distribution
  • Wavelet transform

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