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 Discriminant Analysis (LDA) classifier was trained to differentiate GD from controls based on EEG features. Results: 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 approximate to 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%). Conclusions: 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.