Topographical Pattern Analysis Using Wavelet Based Coherence Connectivity Estimation In The Distinction Of Meditation And Non-Meditation Eeg
Zenodo (CERN European Organization for Nuclear Research) January 25, 2018 DOI: 10.5281/zenodo.1160218 (opens in new tab) via OpenAlex
Summary
AI-generated from the abstractA 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.
Study at a glance
| Characteristics | Conference paper Peer reviewed |
|---|---|
| Topics | Meditation |
| Keywords | Electroencephalography Artificial intelligence Pattern recognition psychology Computer science |
| Key finding | Wavelet transform and AI pattern recognition applied to EEG can detect mental coherence during meditation. |
Abstract
Publication in the conference proceedings of EUSIPCO, Kos island, Greece, 2017