ArticleFrontiers in psychiatry2022
An End-to-End Depression Recognition Method Based on EEGNet.
Article in Frontiers in psychiatry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- A multiscale spatio-temporal convolutional attention network for depressive disorder diagnosis via coarse-grained signal reconstruction based on EEG.Scientific reports · 2026Article
- MCEEGNet: a multi-cue EEG network for quantitative assessment of depression using emotional stimuli-induced EEG signals.Frontiers in artificial intelligence · 2026Article
- AI-assisted multi-modal information for the screening of depression: a systematic review and meta-analysis.NPJ digital medicine · 2025Article
- Oscillatory components of bidirectional cardio-respiratory coupling in depression and suicidal ideation: insights from swarm decomposition and entropy analysis.Frontiers in network physiology · 2025Article
- Constructing a risk screen for attention difficulty in U.S. adults using six machine learning methods.Frontiers in artificial intelligence · 2025Article
- Depressive Disorder Recognition Based on Frontal EEG Signals and Deep Learning.Sensors (Basel, Switzerland) · 2023Article
- Subject-Independent EEG Emotion Recognition Based on Genetically Optimized Projection Dictionary Pair Learning.Brain sciences · 2023Article
- Enhancing generalized anxiety disorder diagnosis precision: MSTCNN model utilizing high-frequency EEG signals.Frontiers in psychiatry · 2023Article
- EEG-based high-performance depression state recognition.Frontiers in neuroscience · 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
4 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Major depressive disorder (MDD) is a common and highly debilitating condition that threatens the health of millions of people. However, current diagnosis of depression relies on questionnaires that are highly correlated with physician experience and hence not completely objective. Electroencephalography (EEG) signals combined with deep learning techniques may be an objective approach to effective diagnosis of MDD. This study proposes an end-to-end deep learning framework for MDD diagnosis based on EEG signals. We used EEG signals from 29 healthy subjects and 24 patients with severe depression to calculate Accuracy, Precision, Recall, F1-Score, and Kappa coefficient, which were 90.98%, 91.27%, 90.59%, and 81.68%, respectively. In addition, we found that these values were highest when happy-neutral face pairs were used as stimuli for detecting depression. Compared with exiting methods for EEG-based MDD classification, ours can maintain stable model performance without re-calibration. The present results suggest that the method is highly accurate for diagnosis of MDD and can be used to develop an automatic plug-and-play EEG-based system for diagnosing depression.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.