Machine Learning Fairness for Depression Detection Using EEG Data

Kwok A. M. H., Cheong J., KALKAN S., Gunes H.

22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025, Texas, United States Of America, 14 - 17 April 2025, (Full Text) identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/isbi60581.2025.10980986
  • City: Texas
  • Country: United States Of America
  • Keywords: Depression Detection, EEG, ML fairness
  • Middle East Technical University Affiliated: No

Abstract

This paper presents the very first attempt to evaluate machine learning fairness for depression detection using electroencephalogram (EEG) data. We conduct experiments using different deep learning architectures such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and Gated Recurrent Unit (GRU) networks across three EEG datasets: Mumtaz, MODMA and Rest. We employ five different bias mitigation strategies at the pre-, in- and post-processing stages and evaluate their effectiveness. Our experimental results show that bias exists in existing EEG datasets and algorithms for depression detection, and different bias mitigation methods address bias at different levels across different fairness measures.