Research graph
References from A data-driven approach to depression detection and assessment using minimal brain signal sensing. Local targets link to admitted publications; unresolved targets remain external evidence.
Unresolved reference
2013 · External reference
Unresolved reference
2008 · External reference
Prevalence of depression, anxiety, and stress during COVID-19 pandemic
10.1055/s-0040-1716442 · 2020 · External reference
Development and performance analysis of machine learning methods for predicting depression among menopausal women
2023 · External reference
Unresolved reference
2024 · External reference
Electroencephalogram (EEG)-based computer-aided technique to diagnose major depressive disorder (MDD)
10.1016/j.bspc.2016.07.006 · 2017 · External reference
An improved empirical mode decomposition of electroencephalogram signals for depression detection
10.1109/taffc.2019.2934412 · 2019 · External reference
A pervasive approach to EEG-based depression detection
10.1155/2018/5238028 · 2018 · External reference
A machine learning based depression screening framework using temporal domain features of the electroencephalography signals
10.1371/journal.pone.0299127 · 2024 · External reference
An optimal channel selection for EEG-based depression detection via kernel-target alignment
2020 · External reference
A major depressive disorder classification framework based on eeg signals using statistical, spectral, wavelet, functional connectivity, and nonlinear analysis
10.1016/j.jneumeth.2021.109209 · 2021 · External reference
Study on feature selection methods for depression detection using three-electrode EEG data
10.1007/s12539-018-0292-5 · 2018 · External reference
Feature-level fusion approaches based on multimodal EEG data for depression recognition
10.1016/j.inffus.2020.01.008 · 2020 · External reference
Brain functional networks based on resting-state EEG data for major depressive disorder analysis and classification
10.1109/tnsre.2020.3043426 · 2020 · External reference
Methods for classifying depression in single channel EEG using linear and nonlinear signal analysis
10.1016/j.cmpb.2017.11.023 · 2018 · External reference
Automated EEG-based screening of depression using deep convolutional neural network
10.1016/j.cmpb.2018.04.012 · 2018 · External reference
Development of wavelet coherence EEG as a biomarker for diagnosis of major depressive disorder
10.1109/jsen.2022.3143176 · 2022 · External reference
Depression identification using eeg signals via a hybrid of lstm and spiking neural networks
10.1109/tnsre.2023.3336467 · 2023 · External reference
Effect of number and placement of EEG electrodes on measurement of neural tracking of speech
10.1371/journal.pone.0246769 · 2021 · External reference
A review of channel selection algorithms for EEG signal processing
10.1186/s13634-015-0251-9 · 2015 · External reference
A hybrid mental health prediction model using support vector machine, multilayer perceptron, and random forest algorithms
2023 · External reference
Validating the wearable MUSE headset for EEG spectral analysis and frontal alpha asymmetry
2021 · External reference
Detecting depression using single-channel EEG and graph methods
10.3390/math10224177 · 2022 · External reference
Where in the brain is depression?
10.1007/s11920-012-0322-7 · 2012 · External reference
Unresolved reference
1996 · External reference
The hospital anxiety and depression scale
10.1111/j.1600-0447.1983.tb09716.x · 1983 · External reference
EEG-based mild depressive detection using feature selection methods and classifiers
10.1016/j.cmpb.2016.08.010 · 2016 · External reference
Depression biomarkers using non-invasive EEG: A review
10.1016/j.neubiorev.2019.07.021 · 2019 · External reference
Nonlinear analysis of EEG in major depression with fractal dimensions
2015 · External reference
Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal
10.1016/j.cmpb.2012.10.008 · 2013 · External reference
EEG power spectra at early stages of depressive disorders
10.1097/wnp.0b013e3181c298fe · 2009 · External reference
Machine learning: Trends, perspectives, and prospects
10.1126/science.aaa8415 · 2015 · External reference
Machine learning and deep learning
10.1007/s12525-021-00475-2 · 2021 · External reference
Support vector machines
10.1109/5254.708428 · 1998 · External reference
Random forests
10.1023/a:1010933404324 · 2001 · External reference
Greedy function approximation: A gradient boosting machine
2001 · External reference
Multimodal depression detection: Fusion of electroencephalography and paralinguistic behaviors using a novel strategy for classifier ensemble
10.1109/jbhi.2019.2938247 · 2019 · External reference
Disease diagnosis in smart healthcare: Innovation, technologies and applications
10.3390/su9122309 · 2017 · External reference
Diagnosing of disease using machine learning
2021 · External reference
Extreme gradient boosting machine learning method for predicting medical treatment in patients with acute bronchiolitis
10.1016/j.bbe.2021.04.015 · 2021 · External reference
Profound and reproducible patterns of reduced regional gray matter characterize major depressive disorder
10.1038/s41398-019-0512-8 · 2019 · External reference
A case-based reasoning model for depression based on three-electrode EEG data
10.1109/taffc.2018.2801289 · 2018 · External reference
A novel depression detection method based on pervasive EEG and eeg splitting criterion
2017 · External reference
Prefrontal cortical-amygdalar metabolism in major depression
10.1111/j.1749-6632.1999.tb09292.x · 1999 · External reference
Unresolved reference
2006 · External reference
Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks
10.1109/tamd.2015.2431497 · 2015 · External reference
Changes of the brain’s bioelectrical activity in cognition, consciousness, and some mental disorders
10.14196/mjiri.31.53 · 2017 · External reference
EEG beta and low gamma power correlates with inattention in patients with major depressive disorder
10.1016/j.jad.2016.06.033 · 2016 · External reference
