Machine Learning Fairness for Depression Detection using EEG Data

2025-01-30

AI Summary

This study addresses the previously unexamined issue of demographic bias—particularly along gender and age dimensions—in EEG-based depression detection models. Method: We propose the first comprehensive fairness evaluation framework spanning preprocessing, in-processing, and post-processing stages, and conduct systematic experiments across three EEG datasets (Mumtaz, MODMA, Rest) using CNN, LSTM, and GRU architectures, coupled with five distinct bias-mitigation techniques. Contribution/Results: Our empirical analysis reveals significant group-level disparities in EEG-based depression classification. Fairness metrics—including equal opportunity and predictive parity—exhibit substantial variation across mitigation strategies, with the best-performing method improving fairness by over 40%. This work establishes the first benchmark framework and evidence-based guidelines for fairness assessment in EEG-driven neuropsychiatric AI diagnostics, advancing trustworthiness and equity in clinical AI applications.

Technology Category

• Machine Learning: Ethics, Bias, and Fairness
• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Computer Vision: Bias, Fairness & Privacy

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

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.

Problem

• Bias Mitigation
• Machine Learning
• Depression Diagnosis

Innovation

• EEG-based Depression Diagnosis
• Bias Mitigation Strategies
• Machine Learning Fairness