MEDITATION EEG INTERPRETATION BASED ON NOVEL FUZZY-MERGING STRATEGIES AND WAVELET FEATURES
Biomedical Engineering Applications Basis and Communications August 25, 2005 DOI: 10.4015/s1016237205000263 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Theoretical or methodological paper Peer reviewed |
|---|---|
| Intervention | Meditation |
| Topics | Meditation |
| Keywords | Wavelet Electroencephalography Interpretation philosophy Artificial intelligence Pattern recognition psychology Fuzzy logic Chart Speech recognition Statistics |
| Citations | 14 |
| Key points | Proposes that novel cluster-managing strategies applied to EEG features can reveal five distinct meditation scenarios differing from those of control subjects. |
Abstract
As 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.