# Cognitively Inspired Federated Learning Framework for Interpretable and Privacy-Secured EEG Biomarker Prediction of Depression Relapse

## TL;DR

This paper presents a privacy-aware and interpretable AI framework using EEG data to predict depression relapse with high accuracy.

## Contribution

A novel federated learning framework combining interpretability and privacy for EEG-based depression relapse prediction.

## Key findings

- The model achieved 92% accuracy, 91% precision, 93% recall, and 90.5% F1-score in predicting depression relapse.
- Region-anchored spectral patterns from attribution maps are linked to relapse risk, offering clinical interpretability.
- The federated setup enables privacy-aware training and multi-site deployment for scalable relapse monitoring.

## Abstract

Depression relapse is a common issue during long-term care. We introduce a privacy-aware explainable personalized federated learning (PFL) framework that incorporates layer-wise relevance propagation and Shapley value analysis to provide patient-specific interpretable predictions from EEG. The study is conducted with the publicly available Healthy Brain Network (HBN) dataset, with analysis conducted for n = 100 subjects with resting-state 128-channel EEG with accompanying psychometric scores, and subject-wise 10-fold cross-validation is used to assess the performance of the model. Multi-channel EEG features and standardized symptom scales are jointly modeled to both increase the clinical context of the model and avoid leakage issues. This results in overall accuracy, precision, recall, and F1-score values of 92%, 91%, 93%, and 90.5%, respectively. The attribution maps from the model suggest region-anchored spectral patterns that are associated with relapse risk, providing clinical interpretability, and the federated setup of the model allows for a privacy-aware training setup that is more easily adaptable to multi-site deployment. Together, these results suggest a scalable and clinically feasible approach to trustworthy relapse monitoring with earlier intervention.