ReviewTranslational neuroscience2022
Machine learning approaches for diagnosing depression using EEG: A review.
Review in Translational neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 39 citations in OpenAlex.
- Electroencephalogram for the Diagnosis of Depression: A Systematic Review and Meta-Analysis of Diagnostic Test Accuracy.Brain topography · 2026Pooled it
- Universum Inspired Graph Contrast Learning for Major Depressive Disorder Identification: In Functional Brain Network View.CNS neuroscience & therapeutics · 2026Article
- An Effective and Interpretable EEG-Based Depression Recognition Method Using Hybrid Feature Selection.Bioengineering (Basel, Switzerland) · 2026Article
- Distinguishing Early Depression from Negative Emotion via Multi-Domain EEG Feature Fusion and Multi-Head Additive Attention Network.Entropy (Basel, Switzerland) · 2026Article
- Research on Depression Recognition Model and Its Temporal Characteristics Based on Multiscale Entropy of EEG Signals.Entropy (Basel, Switzerland) · 2025Article
- Opportunities and Challenges for Clinical Practice in Detecting Depression Using EEG and Machine Learning.Sensors (Basel, Switzerland) · 2025Article
- Psychiatry in the age of AI: transforming theory, practice, and medical education.Frontiers in public health · 2025Review
- Multidimensional EEG features integration with feature selection strategy for precision diagnosis of depressive disorders.Frontiers in psychiatry · 2025Article
- Machine-Learning-Based Depression Detection Model from Electroencephalograph (EEG) Data Obtained by Consumer-Grade EEG Device.Brain sciences · 2024Article
- How to Solve Clinical Challenges in Mood Disorders; Machine Learning Approaches Using Electrophysiological Markers.Clinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology · 2024Review
- Development of Artificial Intelligence for Determining Major Depressive Disorder Based on Resting-State EEG and Single-Pulse Transcranial Magnetic Stimulation-Evoked EEG Indices.Journal of personalized medicine · 2024Article
- Machine Learning Techniques Reveal Aberrated Multidimensional EEG Characteristics in Patients with Depression.Brain sciences · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Depression has become one of the most crucial public health issues, threatening the quality of life of over 300 million people throughout the world. Nevertheless, the clinical diagnosis of depression is now still hampered by behavioral diagnostic methods. Due to the lack of objective laboratory diagnostic criteria, accurate identification and diagnosis of depression remained elusive. With the rise of computational psychiatry, a growing number of studies have combined resting-state electroencephalography with machine learning (ML) to alleviate diagnosis of depression in recent years. Despite the exciting results, these were worrisome of these studies. As a result, ML prediction models should be continuously improved to better screen and diagnose depression. Finally, this technique would be used for the diagnosis of other psychiatric disorders in the future.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.