Electroencephalographic neural correlates and deep learning analysis of a single brief focused mindfulness meditation in young adults: A pilot study.

ABSTRACT:

Objective

This study builds on brief focused mindfulness meditation (BFMM) to examine its associations with physiological indices and electroencephalographic (EEG) neural features related to stress in young adults. In addition, deep learning models are employed to identify complex, nonlinear patterns in EEG signals during BFMM, aiming to determine the most effective classification model.

Methods

Twenty-nine participants (n=29) were enrolled in a before-and-after study of the same cohort. Participants underwent a 10-min resting state, then were instructed to perform BFMM for 10 min. Physiological indices were recorded pre- and post-BFMM, while EEG signals were captured during both the resting and BFMM states. Deep learning techniques including multi-layer perceptron (MLP), lo

SUBMITTER: Luo Y 

PROVIDER: S-EPMC12553933 | biostudies-literature | 2025 Jan-Dec

REPOSITORIES: biostudies-literature