EEG Biomarkers and Explainable AI in Depression, Anxiety, and Bipolar Disorder

Overview

This review highlights the growing integration of explainable artificial intelligence (XAI) with EEG biomarkers to improve diagnosis and understanding of depression, anxiety, and bipolar disorder. It emphasizes how XAI techniques enhance transparency in deep learning models, facilitating clinical trust and personalized care.

Background

Electroencephalography (EEG) is a non-invasive method widely used to study neural dynamics in psychiatric disorders such as depression, anxiety, and bipolar disorder. Traditional EEG analysis methods have limitations in detecting complex brain patterns, prompting the adoption of advanced machine learning and deep learning techniques. However, the 'black box' nature of these models limits clinical adoption, leading to the development of explainable AI (XAI) methods that improve interpretability and support clinical decision-making. The convergence of EEG biomarkers and XAI holds promise for better diagnosis, monitoring, and treatment of these mental health conditions.

Data Highlights

Figures referenced in the review illustrate key concepts: Figure 1 depicts the EEG-XAI pipeline from raw data to interpretable models; Figure 2 contrasts black-box deep learning with XAI approaches using SHAP and LIME for feature importance; Figure 3 shows global mental health disorder prevalence exceeding 16% in some countries; Figure 4 maps age-standardized depressive disorder prevalence worldwide, highlighting regional disparities.

Key Findings

• EEG combined with deep learning models such as CNNs and LSTMs enables effective classification of psychiatric disorders by capturing complex brain signal patterns.
• XAI techniques like SHAP, LIME, and attention mechanisms provide transparency by identifying EEG features critical for diagnosis, enhancing clinical trust.
• Current XAI methods face challenges including high computational costs and underdeveloped evaluation frameworks for clinical utility.
• There is a pressing need for computationally efficient and clinically meaningful XAI approaches tailored to large EEG datasets.
• Ethical and regulatory demands for transparency in healthcare underscore the importance of explainable models for clinician and patient acceptance.
• Global prevalence data highlight the urgent need to prioritize EEG-XAI biomarker research in regions with high burdens of depression and related disorders.

Clinical Implications

Incorporating XAI into EEG-based diagnostic models can improve clinicians' understanding of underlying neural mechanisms and increase confidence in AI-driven decisions. Transparent models facilitate personalized treatment strategies and may enhance monitoring of disease progression and treatment response. However, clinicians should be aware of current limitations in computational efficiency and the need for further validation of XAI methods in clinical settings.

Conclusion

The integration of explainable AI with EEG biomarkers represents a promising frontier to advance diagnosis and management of depression, anxiety, and bipolar disorder. Continued development and clinical validation of interpretable models are essential to realize their full potential in psychiatric care.

References

1. Comprehensive Review on EEG Biomarkers and XAI in Psychiatric Disorders, 2024