• Propose CenterIR, a cluster-aware regularizer for imbalanced EEG regression • Enhance depression severity estimation from resting-state EEG in older adults • Demonstrate stable performance gains over state-of-the-art baselines • Validate robustness through λ -sensitivity and loss ablation analyses Electroencephalography (EEG)-based mental health assessment has gained increasing attention as a non-invasive tool for quantifying depression severity in older adults. However, regression models for continuous severity prediction remain limited, particularly under imbalanced data distributions. This study presents a deep learning framework that integrates convolutional neural networks and bidirectional long short-term memory modules with a novel CenterIR loss to enhance regression performance. Resting-state EEG was recorded from 104 older adults under eyes-open (EO) and eyes-closed (EC) conditions. The proposed model outperformed baseline and state-of-the-art approaches, achieving an MSE of 0.151, MAE of 0.231, and R² of 0.990 in EO, and 0.270, 0.291, and 0.983, respectively, in EC. Paired t-test results indicated significantly better performance in the EO condition, highlighting the potential of EO resting EEG as a reliable neural marker for depression severity. Overall, our framework demonstrates effective imbalanced regression modeling for EEG-based depression assessment in older adults and provides insights for clinical application in mental health monitoring.
Kim et al. (Wed,) studied this question.