# A Novel Demographic Indicator Fusion Network (DIFNet) for Dynamic Fusion of EEG and Demographic Indicators for Robust Depression Detection

## TL;DR

This paper introduces DIFNet, a deep learning model that combines EEG data with demographic factors to improve depression detection accuracy.

## Contribution

The novel DIFNet dynamically fuses EEG and demographic indicators, achieving higher depression detection accuracy than existing methods.

## Key findings

- DIFNet achieves 99.66% accuracy in depression detection by fusing EEG and demographic data.
- Dynamic fusion of age and education improves accuracy by 0.72% over baseline models.
- DIFNet outperforms state-of-the-art methods like SparNet and DBGCN.

## Abstract

Electroencephalography (EEG) has proven to be effective for detecting major depressive disorder (MDD), with deep learning models further advancing its potential. However, the performance of these models may be limited by their neglect of demographic factors (e.g., age, sex, and education), which are known to influence EEG characteristics of depression. To address this, we propose DIFNet, a deep learning framework that dynamically fuses EEG features with demographic indicators (age, sex, and years of education) to enhance depression recognition accuracy. DIFNet is composed of four modules: a multiscale convolutional module, a Transformer encoder module, a temporal convolutional network (TCN) module, and a demographic indicator fusion module. The fusion model leverages convolution to process demographic vectors and integrates them with spatiotemporal EEG features, thereby embedding demographic indicators within the deep learning model for classification. Cross-validation between data trials showed that the DIFNet fusing age and years of education achieves a superior accuracy of 99.66%; the dynamic fusion mechanism improves accuracy by 0.72% compared to the baseline without fusing demographic indicators (98.94%), outperforming state-of-the-art methods (SparNet 94.37% and DBGCN 98.30%).