Resting-state EEG power and machine-learning classification in adult males with gambling disorder.

ABSTRACT:

Background

Gambling disorder (GD) is a behavioral addiction sharing neurobiological features with substance use disorders, yet objective biomarkers remain limited. This study examined resting-state EEG power and applied machine learning to identify potential electrophysiological markers of GD.

Methods

Resting eyes-closed Electroencephalography (EEG) was recorded from 47 individuals with GD and 32 healthy controls. Absolute and relative power across delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz) bands were quantified over eight cortical regions. Group differences and correlations with the South Oaks Gambling Screen (SOGS) were analyzed. Multiple comparisons were controlled using the Benjamini-Hochberg False Discovery Rate (FDR) correction. A Linear Discr

SUBMITTER: Cınaroglu M 

PROVIDER: S-EPMC12835224 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature