# Toward Multi-Dimensional Depression Assessment: EEG-Based Machine Learning and Neurophysiological Interpretation for Diagnosis, Severity, and Cognitive Decline

## TL;DR

This study uses EEG and machine learning to improve the objective diagnosis and severity assessment of depression, identifying key brain connectivity patterns as potential biomarkers.

## Contribution

The first study to use EEG connectivity features to predict depression severity and cognitive impairment.

## Key findings

- The highest classification accuracy for depression diagnosis was 97.66% using 21 features with a KNN classifier.
- ANN regressor achieved strong severity assessment (r2 = 0.89) and cognitive impairment prediction (r2 = 0.89).
- Coherence and PLI values in frontal and temporal pathways across alpha, beta, and gamma sub-bands are critical biomarkers.

## Abstract

Background/Objectives: Depressive disorder (DD) is a prevalent psychiatric condition often diagnosed through subjective self-reports, which can be time-consuming and lead to inaccurate assessments. To enhance diagnostic precision, integrating Electroencephalography (EEG) with machine learning (ML) has gained attention as an objective approach for DD diagnosis and severity assessment. Methods: We propose an interpretable EEG-based ML framework that integrates optimized functional connectivity features, including Coherence, Phase Lag Index (PLI), and Granger causality, to explore EEG-based functional connectivity patterns in individuals clinically diagnosed with depressive DD and to model symptom severity and cognitive vulnerability. The identified biomarkers provide a promising foundation for developing objective, clinically actionable decision-support tools in psychiatric care. Feature selection was performed using the Neighborhood Component Analysis (NCA) method, and biomarkers were identified through statistical tests. Results: The highest classification performance (97.66% ± 2.05%accuracy, 99.20% ± 1.10% sensitivity, 95.91% ± 4.66% specificity, 98.00% ± 1.02% f1-score, and 0.95 ± 0.48 MCC) was achieved using 21 NCA-selected features with a KNN (K = 9) classifier. The best severity assessment (r2 = 0.89 ± 0.10, MSE = 3.96 ± 17.05) and cognitive impairment prediction (r2 = 0.89 ± 0.06, MSE = 0.23 ± 0.45) were obtained using an ANN regressor with 20 and 17 NCA-selected features, respectively. Conclusions: Our approach outperforms previous EEG-based ML models in DD classification and severity prediction using fewer features. Notably, this is the first study to use EEG connectivity features to predict patients’ severity and cognitive impairment in DD. Coherence and PLI values from frontal and temporal pathways across the alpha, beta, and gamma sub-bands may serve as critical biomarkers for DD diagnosis, severity assessment, and prediction of cognitive impairment.