Exploration of EEG-Based Depression Biomarkers Identification Techniques and Their Applications: A Systematic Review

Abstract

Depression is the most common mental illness, which has become the major cause of fear and suicidal mortality or tendencies. Currently, about 10% of the world population has been suffering from depression. The classical approach for detecting depression relies on the clinical questionnaire, which depends on the patients' responses as well as observing their behavioral activities. However, there is no established method to detect depression from EEG biomarkers. Therefore, exploration of EEG biomarkers for depression assessments is vital and has a great potential to improve our understanding and clinical interventions. In this study, we have conducted a systematic review of 52 research articles using the PRISMA-P systematic review protocol, where we analyzed their research methodologies and outcomes. We categorized the experimentations in these articles according to their physical and psychological aspects scaled by the commonly used clinical questionnaire-based assessments. This study finds that the negative stimuli are the better identification strategies for evaluating depression through EEG signals. From this exploration, researchers observed that the Neural Connectivity Analysis and Brain Topological Mapping have huge potentials for finding depression biomarkers, and it is evident that the right-side hemisphere and frontal and parietal-occipital cortex are distinct regions to detect depression using EEG signals. For this mechanism, researchers are using many signal processing and machine learning approaches. In the case of filtering, Independent Component Analysis (ICA) is commonly used to eliminate physiological and non-physiological artifacts. Among machine learning approaches, Convolutional Neural Network (CNN) and Support Vector Machine (SVM) showed better performance for classifying healthy and depressed brains. The authors hope, this study will create an opportunity to explore more in the future for EEG as diagnostic tools by analyzing brain functional connectivity for focusing on clinical interventions.

Concurrent behavioral and electrophysiological longitudinal recordings for in vivo assessment of aging

“…Specific EEG markers have been shown to correlate with AD severity and provide differential dementia diagnosis ( Garn et al, 2015 ; Goossens et al, 2017 ). The value of EEG biomarkers extends to mood disorders such as depression ( Kaiser et al, 2018 ; Dev et al, 2022 ), anxiety disorders ( Pavlenko et al, 2009 ; Al-Ezzi et al, 2021 ), and neurodevelopmental disorders such as Attention-Deficit/Hyperactivity Disorder and Autism Spectrum Disorder ( Wang et al, 2013 ; Matlis et al, 2015 ; Angelidis et al, 2016 ). The use of EEG in brain disorders may complement traditional diagnostic methods of structured interviews and questionnaires ( Snyder et al, 2015 ; Keizer, 2021 ), and the ability to detect and characterize these biomarkers may prove essential for preclinical research.…”

Connectivity-based Meta-Bands: A new approach for automatic frequency band identification in connectivity analyses

“…This study opens the way for personalized, data-driven connectivity analyses, allowing an objective definition of frequency ranges of interest based on the topological similarity of the functional brain network across frequencies. Besides, these analyses, able to quantify the individual idiosyncrasies of neural activity, could be extended to clinical settings to provide a more accurate identification of brain connectivity alterations in several disorders, such as dementia due to Alzheimer's disease, schizophrenia, depression, or migraine [105,106,107,108].…”

Breakdown of long-range spatial correlations of infraslow amplitude fluctuations of EEG oscillations in patients with current and past major depressive disorder

“…The spatial correlation of EEG amplitude fluctuations evaluated in this study measures functional connectivity in the brain. Functional connectivity has been one of the key biomarkers of depression that are found in brain signals (39)(40)(41). In previous amplitude-based FIGURE 5 Differences of spatial correlation of infraslow amplitude fluctuations between current major depressive disorder and control groups.…”