Machine Learning Fairness for Depression Detection using EEG Data
Journal:
arXiv
Published Date:
Jan 30, 2025
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.
