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

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.

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 Discriminant Analysis (LDA) classifier was trained to differentiate GD from controls based on EEG features.

Group differences in EEG power were subtle, with GD showing significantly higher delta power in the left temporal region (p = 0.032, d = 0.43). Within the GD group, greater gambling severity was associated with higher absolute beta power across frontal, parietal, temporal, and occipital regions (r ≈ 0.40-0.50, p < 0.01), and these associations remained significant after FDR correction (pFDR < 0.05). The LDA model using absolute power achieved 73.7% classification accuracy (AUC = 0.74), whereas relative power yielded near-chance accuracy (57.9%).

GD is characterized by subtle but meaningful EEG alterations, particularly increased beta activity linked to gambling severity. Multivariate EEG patterns can distinguish GD from controls, supporting the potential of resting-state EEG as a biomarker for clinical assessment and severity monitoring in behavioral addiction.