Depression biomarkers using non-invasive EEG: A review

Author(s)

de Aguiar Neto, Fernando Soares ^[1] ; Garcia Rosa, Joao Luis ^[1]

Affiliation

^[1] Univ Sao Paulo, Inst Math & Comp Sci, Av Trabalhador Sao Carlense 400, BR-13566590 Sao Carlos, SP - Brazil

• Document type: Review article
• Source: NEUROSCIENCE AND BIOBEHAVIORAL REVIEWS; v. 105, p. 83-93, OCT 2019.
• Web of Science Citations: 1

Abstract

Depression is a serious neurological disorder characterized by strong loss of interest, possibly leading to suicide. According to the World Health Organization, more than 300 million people worldwide suffer from this disorder, being the leading cause of disability. The advancements in electroencephalography (EEG) make it a powerful tool for non-invasive studies on neurological disorders including depression. Scientific community has used EEG to better understand the mechanisms behind the disorder and find biomarkers, which are characteristics that can be precisely measured in order to identify or diagnose a disorder. This work presents a systematic mapping of recent studies ranging from 2014 to the end of 2018 which use non-invasive EEG to detect depression biomarkers. Our research has analyzed more than 250 articles and we discuss the findings and promising biomarkers of 42 studies, finding that the depressed brain appear to have a more random network structure, also finding promising features for diagnostic, such as, gamma band and signal complexity; among others which may detect specific depression-related symptoms such as suicidal ideation. (AU)

• FAPESP's process: 16/02555-8 - Development of algorithms and computational techniques for application in brain-computer interfaces
• Grantee: João Luís Garcia Rosa
• Support Opportunities: Regular Research Grants