"We firstly adopted wavelet transform to decompose EEG signals and reconstruct waves in each frequency band using wavelet coefficients. From the power ratio, we selected the candidates (normalized alpha-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 coeffiicents to decide the number of clusters."
They found that (1) the alpha power in the control group decreased dramatically but not in the experimental group, (2) after meditation alpha power in the frontal area of meditators increased more than that of the control subjects (after resting-EEG recording). The authors speculated that activating the medial prefrontal cortex and the anterior cingulate cortex during meditation may be the reason for increasing frontal alpha power.
Liu, Chuan-Yi, and Pei-Chen Lo. "Investigation of spatial characteristics of meditation EEG using wavelet analysis and fuzzy classifier." Proceedings of the fifth IASTED International Conference: biomedical engineering. ACTA Press, 2007.