Prediction of acoustic tinnitus suppression using resting-state EEG via explainable AI approach.

ABSTRACT: Tinnitus is defined as the perception of sound without an external source. Its perceptual suppression or on/off states remain poorly understood. This study investigates neural traits linked to brief acoustic tinnitus suppression (BATS) using naive resting-state EEG (closed eyes) from 102 individuals. A set of EEG features (band power, entropy, aperiodic slope and offset of the EEG spectrum, and connectivity) and standard classifiers were applied achieving consistent high accuracy across data splits: 98% for sensor and 86% for source models. The Random Forest model outperformed other classifiers by excelling in robustness and reduction of overfitting. It identified several key EEG features, most prominently alpha and gamma frequency band power. Gamma power was stronger in the left auditory

SUBMITTER: Shabestari PS 

PROVIDER: S-EPMC11958676 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature