EEG-based characterization of auditory attention and meditation: an ERP and machine learning approach.

ABSTRACT:

Introduction

This scientific investigation explored how meditation influences neural sound stimulus responses by employing EEG techniques during both meditative states and auditory oddball tasks. The study evaluated event-related potentials alongside theta, alpha and beta spectral power while employing machine learning techniques to distinguish meditative states from cognitive tasks.

Methods

The study utilized data from 13 participants aged 24-58, which researchers obtained through an openly accessible OpenNeuro dataset.

Result

Examination of eventrelated potentials (ERPs) demonstrated that P300 amplitude showed significant growth when responding to oddball stimuli, which indicates increased attention allocation (p < 0.05). Spectral power analysis demonstrated

SUBMITTER: Attar ET 

PROVIDER: S-EPMC12417730 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature