Poincaré plots, a tool for analyzing non-linear patterns in biological signals, can detect changes in brain activity during meditation. In sixteen healthy women, the width of Poincaré plots of EEG signals increased as the time lag between data points grew from one to six, indicating that meditation alters short-term variability in brain signals. This method offers a simple, quantitative way to evaluate EEG data collected over short periods and adapts well to the chaotic nature of such signals.
An algorithm that distinguishes between resting and meditative states using electroencephalogram (EEG) signals was tested. EEG data from 25 healthy women were collected before and during meditation. Wavelet coefficients and correlation dimensions from electrodes Fz, Cz, and Pz were used as features for several classifiers. The Fisher discriminant and Parzen classifier achieved the highest accuracies: 85.02% and 84.75% with Wavelet coefficients, and 92.37% for both when using correlation dimensions. The findings suggest that nonlinear measures like correlation dimension are more effective than wavelet features for classifying meditation versus rest from EEG signals.