Abstract
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 electroencephalo-graphic (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.
Highlights
Meditation techniques are often used to influence stress-dependent diseases, such as anxiety, hypertensive disorders or coronary disease, tension headaches or dependencies
Longitudinal studies demonstrate that meditation practice significantly changes early stimuli processing, which leads to improved dynamics and flexibility of brain functions related to attention
Improved dynamics and flexibility of brain functions are achieved by a more flexible allocation of attention resources that most likely modulate early processing based on independent stimuli
Summary
Meditation techniques are often used to influence stress-dependent diseases, such as anxiety, hypertensive disorders or coronary disease, tension headaches or dependencies. If attention is no longer focused on the meditation object, the “default mode” is activated, including the posterior cingulate, medial prefrontal cortex, posterior lateral parietal temporal cortex and parahippocampal gyrus [1]. The cooperation between these brain areas may, to a certain degree, be estimated using the similarities of EEG signals detected by the electrodes placed over these areas. This study compares the EEG signal complexity measures in experienced meditators (more than 1000 hours of meditation practice) before and during meditation Under vipassana meditation, both types of attention regulation are used and are complementary to each other. In the later stages of meditation practice, a greater emphasis is placed on mindfulness
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