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Major Depressive Disorder (MDD) is a prevalent yet challenging condition to diagnose due to its reliance on subjective clinical assessments. This study presents a novel hybrid machine learning approach with electroencephalogram (EEG) data to enhance diagnostic accuracy and interpretability. The MODMA dataset is used alongside synthetic data generated through Conditional GANs and Gaussian Mixture Models to improve generalization. A comprehensive feature extraction process captures linear, non-linear, and time-frequency characteristics of EEG signals. The proposed hybrid model integrates multiple machine learning algorithms through majority voting, achieving robust and accurate MDD classification. By addressing diagnostic limitations, this research contributes to developing scalable, reliable tools for early, personalized MDD detection.

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EEG-Based Prediction of Major Depressive Disorder with Hybrid Machine Learning Models and Synthetic Data Augmentation

  • 会议论文

  • 发表日期：

    2026年

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  • Camila F. S. Campos,
  • João Fausto L. de Oliveira,
  • Fernando B. L. Neto,
  • João Guilherme R. de Carvalho

摘要

Major Depressive Disorder (MDD) is a prevalent yet challenging condition to diagnose due to its reliance on subjective clinical assessments. This study presents a novel hybrid machine learning approach with electroencephalogram (EEG) data to enhance diagnostic accuracy and interpretability. The MODMA dataset is used alongside synthetic data generated through Conditional GANs and Gaussian Mixture Models to improve generalization. A comprehensive feature extraction process captures linear, non-linear, and time-frequency characteristics of EEG signals. The proposed hybrid model integrates multiple machine learning algorithms through majority voting, achieving robust and accurate MDD classification. By addressing diagnostic limitations, this research contributes to developing scalable, reliable tools for early, personalized MDD detection.