Depression diagnosis: EEG-based cognitive biomarkers and machine learning

• Published in: Behavioural brain research 2025-02, Vol.478, p.115325, Article 115325
• Main Authors: Boby, Kiran, Veerasingam, Sridevi
• Format: Article
• Language: English
• Subjects: Biomarkers; Brain - physiopathology; Cognition; Cognition - physiology; Deep learning; Depression; Depression - diagnosis; Depression - physiopathology; Depressive Disorder - diagnosis; Depressive Disorder - physiopathology; EEG; Electroencephalography - methods; Humans; Machine Learning
• ISSN: 0166-4328; 1872-7549; 1872-7549
• DOI: 10.1016/j.bbr.2024.115325

Depression is a complex mental illness that has significant effects on people as well as society. The traditional techniques for the diagnosis of depression, along with the potential of nascent biomarkers especially EEG-based biomarkers, are studied. This review explores the significance of cognitive biomarkers, offering a comprehensive understanding of their role in the overall assessment of depression. It also investigates the effects of depression on various brain regions, outlines promising areas for future research, and emphasizes the importance of understanding the neurophysiological roots of depression. Furthermore, it elucidates how machine learning and deep learning models are integrated into EEG-based depression diagnosis, emphasizing their importance in optimizing personalized therapeutic protocols and improving diagnostic accuracy with EEG data analysis. [Display omitted] •Depression diagnosis is improved by using biomarkers.•Electroencephalogram-based biomarkers help understand mental, and emotional states.•Depression severity leads to cognitive impairments and biases.•Electroencephalogram frameworks are efficient in detecting cognitive impairment.•Machine learning techniques revolutionize depression diagnosis and prediction.