Machine Learning Fairness for Depression Detection using EEG Data

• URL: http://arxiv.org/abs/2501.18192v1
• Date: Thu, 30 Jan 2025 08:13:01 GMT
• Title: Machine Learning Fairness for Depression Detection using EEG Data
• Authors: Angus Man Ho Kwok, Jiaee Cheong, Sinan Kalkan, Hatice Gunes,
• Abstract summary: 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 CNN, Long Short-Term Memory (LSTM) networks, and Gated Recurrent Unit (GRU) networks. 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.
• Score: 14.61416119202288
• License:
• 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.

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