Journal Article2022Cited by 110

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

CHIRANJEEB BISWAS, HELAL UDDIN AHMED, MD ASHRAFUL AMIN, FARHANA SARKER, RAVI VAIDYANATHAN, KHONDAKER A MAMUN

IEEE Access

Institute of Electrical and Electronics Engineers (IEEE), Vol. 10, pp. 16756-16781, ISBN: 2169-3536

  • Good Health and Well-being — Relevance: 84%
DOI: 10.1109/access.2022.3146711Open AccessURL

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 tool by analyzing brain functional connectivity for focusing on clinical interventions.

Keywords

  • EEG biomarkers
  • independent component analysis
  • depression
  • convolutional neural networks
  • Support vector machine
  • depression detection
  • brain functional connectivity
  • EEG artifact removal
  • negative stimuli

CCDS Authors

Prof. M Ashraful Amin, PhD
M Ashraful Amin, PhD

Prof. M Ashraful Amin, PhD

  • Founder Director, CCDS
  • Director, AI/ML, HCI & DS Wings

Professor

+1 more affiliation

Department of Information Sciences and Technology

George Mason University, USA

Published at

ACL, CHI, UbiComp, ECAI, WACV, PAKDD, IJCNN, ICPR, ICIP, HCII, CLEF, BLP, Scientific Reports, JMIR, IEEE Access, Smart Agricultural Technology, Frontiers in Computational Neuroscience, Diabetes Research and Clinical Practice, PLOS ONE

Research Interests

Machine Learning, Cognitive & Vision Science, Cybernetics, Surveillance & Security, ICT in Education, Health, & Agriculture, Human-Computer Interaction, Internet of Things, Robotics

Farhana Sarker, PhD
Farhana Sarker, PhD

Farhana Sarker, PhD

  • Director, WiSE Wing

Associate Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

Published at

ICDMW, IEEE Access

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