Non-Linear EEG Measures in Meditation

In this study, the performance of Sevcik’s algorithm that calculates the fractal dimension and permutation entropy as discriminants to detect calming and insight meditation in electroencephalographic (EEG) signals was assessed. The proposed methods were applied to EEG recordings from meditators practicing insight meditation and calming meditation before as well as during both types of meditation. Analysis was conducted using statistical hypothesis testing to determine the validity of the proposed meditation-identifying techniques. For both types of meditation, there was a statistically significant reduction in the permutation entropy. This result can be explained by the increased EEG synchronization, which is repeatedly observed in the course of meditation. In contrast, the fractal dimension (FD) was significantly increased during calming meditation, but during insight meditation, no statistically significant change was detected. Increased FD during meditation can be interpreted as an increase in self-similarity of EEG signals during self-organisation of hierarchical structure oscillators in the brain. Our results indicate that fractal dimension and permutation entropy could be used as parameters to detect both types of meditation. The permutation entropy is advantageous compared with the fractal dimension because it does not require a stationary signal.