A Systematic Review of Mental Health Monitoring and Intervention Using Unsupervised Deep Learning on EEG Data

Electroencephalography (EEG) is a widely used non-invasive method for capturing brain activity, offering valuable insights into cognitive and emotional states relevant to mental health. With the growing complexity and volume of EEG data, machine learning (ML) techniques—particularly deep learning—have become integral in extracting meaningful patterns. While much of the current literature focuses on supervised learning methods that rely on labeled data, unsupervised learning offers an alternative approach capable of discovering hidden structures and novel biomarkers without requiring predefined labels. This systematic review aimed to identify and synthesize recent peer-reviewed research that applied unsupervised or self-supervised learning techniques to EEG data in the context of mental health monitoring, diagnosis, or analysis. A comprehensive search was conducted across six major databases, including PubMed, Scopus, Web of Science, IEEE Xplore, PsycINFO, and Google Scholar, covering literature from January 2018 to March 2025. Following PRISMA guidelines, predefined inclusion and exclusion criteria were applied to screen and assess the relevance and quality of studies. From 512 initial records, 403 unique articles were screened, and 20 underwent full-text review. Ultimately, no studies met all the inclusion criteria. Most were excluded for employing only supervised methods, being review articles, or focusing on non-mental-health applications. The absence of eligible studies highlights a significant gap in current research and emphasizes the need for future empirical work exploring unsupervised techniques in EEG-based mental health applications. Such efforts could pave the way for more scalable, label-free approaches to understanding brain dynamics in psychological conditions.