A method using electroencephalography (EEG) and wavelet transform combined with artificial intelligence pattern recognition can detect moments of mental coherence during meditation. The approach processes EEG signals through wavelet decomposition to identify neural patterns associated with a focused, coherent mental state. Results suggest that this computational technique reliably distinguishes coherent from non-coherent brain activity, offering a potential tool for objective meditation assessment.
Electroencephalography (EEG) signals recorded during Kriya Yoga meditation were analyzed using two brain connectivity measures: partial directed coherence (PDC) and directed transfer function (DTF). PDC performed more efficiently than DTF in most cases when compared on absolute energy, signal-to-noise ratio, and relative signal-to-noise ratio scales. PDC provided a better understanding of non-symmetric neural relations in meditation EEG. The time-varying multivariate autoregressive model can track neurodynamical changes better than other methods. The authors note that further investigation is needed to warrant the claim that PDC is superior.
Phase synchrony analysis of EEG signals from 23 meditators during meditation reveals that an Improved Phase Locking Value (IPLV) method outperforms the standard Phase Locking Value (PLV) in detecting neural synchrony. Neural synchrony modulated the EEG signals independently, offering a better interpretation of functional connectivity between cortical areas during meditation. The work uses phase synchrony to study simultaneous peaks and valleys in EEG activity, capturing transient spectral perturbations across different brain regions.