This paper is in the following e-collection/theme issue:

Artificial Intelligence (1371) Machine Learning (1465) Depression and Mood Disorders; Suicide Prevention (1363)

Published on in Vol 22, No 11 (2020): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/19548, first published .
Classification of Depression Through Resting-State Electroencephalogram as a Novel Practice in Psychiatry: Review

Classification of Depression Through Resting-State Electroencephalogram as a Novel Practice in Psychiatry: Review

Classification of Depression Through Resting-State Electroencephalogram as a Novel Practice in Psychiatry: Review

Authors of this article:

Milena Čukić1 Author Orcid Image ;   Victoria López2 Author Orcid Image ;   Juan Pavón2 Author Orcid Image
Article Authors Cited by (29) Tweetations (3) Metrics

Journals

  1. Llamocca P, López V, Santos M, Čukić M. Personalized Characterization of Emotional States in Patients with Bipolar Disorder. Mathematics 2021;9(11):1174 View
  2. Li M, Liu Y, Liu Y, Pu C, Yin R, Zeng Z, Deng L, Wang X. Resting-state EEG-based convolutional neural network for the diagnosis of depression and its severity. Frontiers in Physiology 2022;13 View
  3. Jan D, de Vega M, López-Pigüi J, Padrón I. Applying Deep Learning on a Few EEG Electrodes during Resting State Reveals Depressive States: A Data Driven Study. Brain Sciences 2022;12(11):1506 View
  4. Wu C, Huang H, Huang S, Chen I, Liao S, Chen C, Lin C, Lee S, Chen M, Tsai C, Weng C, Ko L, Jung T, Liu Y. Resting-State EEG Signal for Major Depressive Disorder Detection: A Systematic Validation on a Large and Diverse Dataset. Biosensors 2021;11(12):499 View
  5. Sukholeister O, Nakonechnyi A. RECOGNITION OF MENTAL DISORDERS FROM PHYSIOLOGICAL SIGNALS ANALYSIS. Measuring Equipment and Metrology 2022;83(4):11 View
  6. Iyer R, Nedeljkovic M, Meyer D. Using Voice Biomarkers to Classify Suicide Risk in Adult Telehealth Callers: Retrospective Observational Study. JMIR Mental Health 2022;9(8):e39807 View
  7. Čukić M, Savić D, Sidorova J. When Heart Beats Differently in Depression: Review of Nonlinear Heart Rate Variability Measures. JMIR Mental Health 2023;10:e40342 View
  8. Shim M, Im C, Lee S, Hwang H. Enhanced Performance by Interpretable Low-Frequency Electroencephalogram Oscillations in the Machine Learning-Based Diagnosis of Post-traumatic Stress Disorder. Frontiers in Neuroinformatics 2022;16 View
  9. Čukić M, López V. Progress in Objective Detection of Depression and Online Monitoring of Patients Based on Physiological Complexity. Frontiers in Psychiatry 2022;13 View
  10. Liu Y, Pu C, Xia S, Deng D, Wang X, Li M. Machine learning approaches for diagnosing depression using EEG: A review. Translational Neuroscience 2022;13(1):224 View
  11. Llamocca P, López V, Čukić M. The Proposition for Bipolar Depression Forecasting Based on Wearable Data Collection. Frontiers in Physiology 2022;12 View
  12. Khosla A, Khandnor P, Chand T. Automated diagnosis of depression from EEG signals using traditional and deep learning approaches: A comparative analysis. Biocybernetics and Biomedical Engineering 2022;42(1):108 View
  13. Bhadra S, Kumar C. An insight into diagnosis of depression using machine learning techniques: a systematic review. Current Medical Research and Opinion 2022;38(5):749 View
  14. Seal A, Bajpai R, Karnati M, Agnihotri J, Yazidi A, Herrera-Viedma E, Krejcar O. Benchmarks for machine learning in depression discrimination using electroencephalography signals. Applied Intelligence 2023;53(10):12666 View
  15. Yang B, Huang Y, Li Z, Hu X. Management of post-stroke depression (PSD) by electroencephalography for effective rehabilitation. Engineered Regeneration 2023;4(1):44 View
  16. Liu X, Ji X, Weng X, Zhang Y. Artificial intelligence ecosystem for computational psychiatry: Ideas to practice. World Journal of Meta-Analysis 2023;11(4):79 View
  17. Mehrabbeik M, Shams-Ahmar M, Sabourin C, Jafari S, Lomber S, Merrikhi Y. Detecting memory content in firing rate signals using a machine learning approach: A fractal analysis. Biomedical Signal Processing and Control 2023;85:104945 View
  18. Zeng J, Zhang Y, Xiang Y, Liang S, Xue C, Zhang J, Ran Y, Cao M, Huang F, Huang S, Deng W, Li T. Optimizing multi-domain hematologic biomarkers and clinical features for the differential diagnosis of unipolar depression and bipolar depression. npj Mental Health Research 2023;2(1) View
  19. Pacia S. Sub-Scalp Implantable Telemetric EEG (SITE) for the Management of Neurological and Behavioral Disorders beyond Epilepsy. Brain Sciences 2023;13(8):1176 View
  20. Shao X, Ying M, Zhu J, Li X, Hu B. Achieving EEG-based depression recognition using Decentralized-Centralized structure. Biomedical Signal Processing and Control 2024;95:106402 View
  21. Zheng S, Zeng W, Wu Q, Li W, He Z, Li E, Tang C, Xue X, Qin G, Zhang B, Yin H, Ai S. Predictive Models for Suicide Attempts in Major Depressive Disorder and the Contribution of EPHX2: A Pilot Integrative Machine Learning Study. Depression and Anxiety 2024;2024:1 View
  22. Qu S, Wang D, Yan C, Chu N, Li Z, Luo G, Chen H, Liu X, Zhang X, Dong Q, Li X, Sun S, Hu B. Depression recognition using high-order generalized multilayer brain functional network fused with EEG multi-domain information. Information Fusion 2025;114:102723 View

Books/Policy Documents

  1. Naregalkar P, Shinde A. Optical and Wireless Technologies. View
  2. Ghattas P, Gamal M, Eldawlatly S. Brain Informatics. View
  3. Čukić M, Olejarzcyk E, Bachmann M. The Fractal Geometry of the Brain. View
  4. Neff P, Meyer M. Textbook of Tinnitus. View