MEDITATION EEG INTERPRETATION BASED ON NOVEL FUZZY-MERGING STRATEGIES AND WAVELET FEATURES

Meditation alters brain electrical activity in ways that can be detected with EEG. This paper introduces a new automated method for scoring meditation stages from EEG recordings, using wavelet analysis and fuzzy c-means clustering with novel cluster-management strategies to produce interpretations that more closely match expert visual inspection. The resulting gray-scale chart of EEG features reveals five distinct meditation scenarios that differ from those of control subjects.

Study at a glance

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.