A systematic review of EEG-based biomarkers for depression, anxiety, and bipolar disorder: trends in explainable artificial intelligence (XAI) - Takeaways - MDSpire
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A Comprehensive Review of EEG Biomarkers for Depression, Anxiety, and Bipolar Disorder: Insights into Explainable Artificial Intelligence (XAI) Trends

  • By

  • Lijiang Zhai

  • Min Zhao

  • Jia Zhang

  • Muhammad Jamil

  • Rida Naz

  • Caina Li

  • December 25, 2025

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  • 1

    EEG is a non-invasive technique that reveals neural dynamics in psychiatric disorders like depression, anxiety, and bipolar disorder.

  • 2

    Deep learning models, including CNNs and LSTMs, enhance EEG analysis by decoding complex brain states for psychiatric classification.

  • 3

    Explainable AI (XAI) methods improve the interpretability of deep learning models, fostering clinical trust and supporting decision-making.

  • 4

    The integration of XAI techniques in EEG research aims to enhance diagnostic accuracy and treatment outcomes for mental health disorders.

  • 5

    There is a critical need for computationally efficient XAI approaches to ensure clinical utility and transparency in psychiatric EEG analysis.

Original Source(s)

A systematic review of EEG-based biomarkers for depression, anxiety, and bipolar disorder: trends in explainable artificial intelligence (XAI)

  • by Lijiang Zhai, Min Zhao, Jia Zhang, Muhammad Jamil, Rida Naz, Caina Li

  • December 25, 2025

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