ArticleBrain sciences2022
A Depression Prediction Algorithm Based on Spatiotemporal Feature of EEG Signal.
Article in Brain sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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
12 citing papers in PubMed.
- Depression Detection from Three-Channel Resting-State EEG Using a Hybrid Conv1D and Spectral-Statistical Fusion Model.Sensors (Basel, Switzerland) · 2026Article
- Interpretable machine-learning for depression classification based on a three-component EEG marker.Frontiers in computational neuroscience · 2026Article
- Mental health monitoring in 5G Edge-Enabled Cognitive IoT with Temporal Shift Transformer and integrated Stackelberg Game Theory and Nomadic People Optimizer.Scientific reports · 2025Article
- A novel spatiotemporal model with advanced feature extraction and unified brain network for depression detection using electroencephalogram signals.Royal Society open science · 2025Article
- Multichannel convolutional transformer for detecting mental disorders using electroancephalogrpahy records.Scientific reports · 2025Article
- A Lightweight Multi-Mental Disorders Detection Method Using Entropy-Based Matrix from Single-Channel EEG Signals.Brain sciences · 2024Article
- A machine learning based depression screening framework using temporal domain features of the electroencephalography signals.PloS one · 2024Article
- Depressive Disorder Recognition Based on Frontal EEG Signals and Deep Learning.Sensors (Basel, Switzerland) · 2023Article
- Electroencephalography-Based Depression Detection Using Multiple Machine Learning Techniques.Diagnostics (Basel, Switzerland) · 2023Article
- Modern Methods of Diagnostics and Treatment of Neurodegenerative Diseases and Depression.Diagnostics (Basel, Switzerland) · 2023Review
- Machine Learning Model for Computer-Aided Depression Screening among Young Adults Using Wireless EEG Headset.Computational intelligence and neuroscience · 2023Article
- Applying Deep Learning on a Few EEG Electrodes during Resting State Reveals Depressive States. A Data Driven Study.Brain sciences · 2022Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Depression has gradually become the most common mental disorder in the world. The accuracy of its diagnosis may be affected by many factors, while the primary diagnosis seems to be difficult to define. Finding a way to identify depression by satisfying both objective and effective conditions is an urgent issue. In this paper, a strategy for predicting depression based on spatiotemporal features is proposed, and is expected to be used in the auxiliary diagnosis of depression. Firstly, electroencephalogram (EEG) signals were denoised through the filter to obtain the power spectra of the three corresponding frequency ranges, Theta, Alpha and Beta. Using orthogonal projection, the spatial positions of the electrodes were mapped to the brainpower spectrum, thereby obtaining three brain maps with spatial information. Then, the three brain maps were superimposed on a new brain map with frequency domain and spatial characteristics. A Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) were applied to extract the sequential feature. The proposed strategy was validated with a public EEG dataset, achieving an accuracy of 89.63% and an accuracy of 88.56% with the private dataset. The network had less complexity with only six layers. The results show that our strategy is credible, less complex and useful in predicting depression using EEG signals.
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.