A Systematic Review of EEG-based Machine Intelligence Algorithms for Depression Diagnosis, and Monitoring
Journal:
arXiv
Published Date:
Mar 25, 2025
Abstract
Depression disorder is a serious health condition that has affected the lives
of millions of people around the world. Diagnosis of depression is a
challenging practice that relies heavily on subjective studies and, in most
cases, suffers from late findings. Electroencephalography (EEG) biomarkers have
been suggested and investigated in recent years as a potential transformative
objective practice. In this article, for the first time, a detailed systematic
review of EEG-based depression diagnosis approaches is conducted using advanced
machine learning techniques and statistical analyses. For this, 938 potentially
relevant articles (since 1985) were initially detected and filtered into 139
relevant articles based on the review scheme 'preferred reporting items for
systematic reviews and meta-analyses (PRISMA).' This article compares and
discusses the selected articles and categorizes them according to the type of
machine learning techniques and statistical analyses. Algorithms, preprocessing
techniques, extracted features, and data acquisition systems are discussed and
summarized. This review paper explains the existing challenges of the current
algorithms and sheds light on the future direction of the field. This
systematic review outlines the issues and challenges in machine intelligence
for the diagnosis of EEG depression that can be addressed in future studies and
possibly in future wearable technologies.
