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For: Ding X, Yue X, Zheng R, Bi C, Li D, Yao G. Classifying major depression patients and healthy controls using EEG, eye tracking and galvanic skin response data. Journal of Affective Disorders 2019;251:156-61. [DOI: 10.1016/j.jad.2019.03.058] [Cited by in Crossref: 15] [Cited by in F6Publishing: 26] [Article Influence: 5.0] [Reference Citation Analysis]
Number Citing Articles
1 Othmani A, Zeghina A, Muzammel M. A Model of Normality Inspired Deep Learning Framework for Depression Relapse Prediction Using Audiovisual Data. Computer Methods and Programs in Biomedicine 2022;226:107132. [DOI: 10.1016/j.cmpb.2022.107132] [Reference Citation Analysis]
2 Höller Y, Jónsdóttir ST, Hannesdóttir AH, Ólafsson RP. EEG-responses to mood induction interact with seasonality and age. Front Psychiatry 2022;13:950328. [DOI: 10.3389/fpsyt.2022.950328] [Reference Citation Analysis]
3 Abenna S, Nahid M, Bouyghf H, Ouacha B. EEG-based BCI: A novel improvement for EEG signals classification based on real-time preprocessing. Computers in Biology and Medicine 2022. [DOI: 10.1016/j.compbiomed.2022.105931] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
4 Qin X, Xiong J, Cui R, Zou G, Long C, Lei X. EEG microstate temporal Dynamics Predict depressive symptoms in College Students. Brain Topogr 2022. [PMID: 35790705 DOI: 10.1007/s10548-022-00905-0] [Reference Citation Analysis]
5 Singh J, Hamid MA. Cognitive Computing in Mental Healthcare: a Review of Methods and Technologies for Detection of Mental Disorders. Cogn Comput. [DOI: 10.1007/s12559-022-10042-2] [Reference Citation Analysis]
6 Ahmed T, Qassem M, Kyriacou PA. Physiological monitoring of stress and major depression: A review of the current monitoring techniques and considerations for the future. Biomedical Signal Processing and Control 2022;75:103591. [DOI: 10.1016/j.bspc.2022.103591] [Reference Citation Analysis]
7 Höller Y, Urbschat MM, Kristófersson GK, Ólafsson RP. Predictability of Seasonal Mood Fluctuations Based on Self-Report Questionnaires and EEG Biomarkers in a Non-clinical Sample. Front Psychiatry 2022;13:870079. [DOI: 10.3389/fpsyt.2022.870079] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
8 Zhang D, Liu X, Xu L, Li Y, Xu Y, Xia M, Qian Z, Tang Y, Liu Z, Chen T, Liu H, Zhang T, Wang J. Effective differentiation between depressed patients and controls using discriminative eye movement features. J Affect Disord 2022;307:237-43. [PMID: 35390355 DOI: 10.1016/j.jad.2022.03.077] [Reference Citation Analysis]
9 Chen X, Xu L, Pan Z. Design and Preliminary Realization of a Screening and Early Warning Health Management System for Populations at High Risk for Depression. Int J Environ Res Public Health 2022;19:3599. [PMID: 35329284 DOI: 10.3390/ijerph19063599] [Reference Citation Analysis]
10 Bhadra S, Kumar CJ. An insight into diagnosis of depression using machine learning techniques: a systematic review. Curr Med Res Opin 2022;:1-62. [PMID: 35129401 DOI: 10.1080/03007995.2022.2038487] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
11 Lim JZ, Mountstephens J, Teo J. Eye-Tracking Feature Extraction for Biometric Machine Learning. Front Neurorobot 2022;15:796895. [DOI: 10.3389/fnbot.2021.796895] [Reference Citation Analysis]
12 Ding N, Zhong Y, Li J, Xiao Q. Study on selection of native greening plants based on eye-tracking technology. Sci Rep 2022;12. [DOI: 10.1038/s41598-022-05114-0] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
13 Diao Y, Geng M, Fu Y, Wang H, Liu C, Gu J, Dong J, Mu J, Liu X, Wang C. A combination of P300 and eye movement data improves the accuracy of auxiliary diagnoses of depression. J Affect Disord 2022;297:386-95. [PMID: 34710500 DOI: 10.1016/j.jad.2021.10.028] [Reference Citation Analysis]
14 Edughele HO, Zhang Y, Muhammad-sukki F, Vien Q, Morris-cafiero H, Opoku Agyeman M. Eye-Tracking Assistive Technologies for Individuals With Amyotrophic Lateral Sclerosis. IEEE Access 2022;10:41952-72. [DOI: 10.1109/access.2022.3164075] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
15 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;42:108-42. [DOI: 10.1016/j.bbe.2021.12.005] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
16 Zhao S, Bao Z, Zhao X, Xu M, Li MD, Yang Z. Identification of Diagnostic Markers for Major Depressive Disorder Using Machine Learning Methods. Front Neurosci 2021;15:645998. [PMID: 34220416 DOI: 10.3389/fnins.2021.645998] [Cited by in Crossref: 1] [Cited by in F6Publishing: 4] [Article Influence: 1.0] [Reference Citation Analysis]
17 Uyulan C, de la Salle S, Erguzel TT, Lynn E, Blier P, Knott V, Adamson MM, Zelka M, Tarhan N. Depression Diagnosis Modeling With Advanced Computational Methods: Frequency-Domain eMVAR and Deep Learning. Clin EEG Neurosci 2021;:15500594211018545. [PMID: 34080925 DOI: 10.1177/15500594211018545] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
18 Ahmed A, Misrani A, Tabassum S, Yang L, Long C. Minocycline inhibits sleep deprivation-induced aberrant microglial activation and Keap1-Nrf2 expression in mouse hippocampus. Brain Res Bull 2021;174:41-52. [PMID: 34087360 DOI: 10.1016/j.brainresbull.2021.05.028] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
19 Klaib AF, Alsrehin NO, Melhem WY, Bashtawi HO, Magableh AA. Eye tracking algorithms, techniques, tools, and applications with an emphasis on machine learning and Internet of Things technologies. Expert Systems with Applications 2021;166:114037. [DOI: 10.1016/j.eswa.2020.114037] [Cited by in Crossref: 19] [Cited by in F6Publishing: 17] [Article Influence: 19.0] [Reference Citation Analysis]
20 Greco C, Matarazzo O, Cordasco G, Vinciarelli A, Callejas Z, Esposito A. Discriminative Power of EEG-Based Biomarkers in Major Depressive Disorder: A Systematic Review. IEEE Access 2021;9:112850-70. [DOI: 10.1109/access.2021.3103047] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 4.0] [Reference Citation Analysis]
21 Zhao M, Feng Z. Machine Learning Methods to Evaluate the Depression Status of Chinese Recruits: A Diagnostic Study. Neuropsychiatr Dis Treat 2020;16:2743-52. [PMID: 33209029 DOI: 10.2147/NDT.S275620] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
22 Fernandes A, Van Lenthe FJ, Vallée J, Sueur C, Chaix B. Linking physical and social environments with mental health in old age: a multisensor approach for continuous real-life ecological and emotional assessment. J Epidemiol Community Health 2021;75:477-83. [PMID: 33148684 DOI: 10.1136/jech-2020-214274] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
23 Li H, Cui L, Cao L, Zhang Y, Liu Y, Deng W, Zhou W. Identification of bipolar disorder using a combination of multimodality magnetic resonance imaging and machine learning techniques. BMC Psychiatry 2020;20:488. [PMID: 33023515 DOI: 10.1186/s12888-020-02886-5] [Cited by in Crossref: 2] [Cited by in F6Publishing: 5] [Article Influence: 1.0] [Reference Citation Analysis]
24 Ding X, Li Y, Li D, Li L, Liu X. Using machine-learning approach to distinguish patients with methamphetamine dependence from healthy subjects in a virtual reality environment. Brain Behav 2020;10:e01814. [PMID: 32862513 DOI: 10.1002/brb3.1814] [Cited by in Crossref: 7] [Cited by in F6Publishing: 8] [Article Influence: 3.5] [Reference Citation Analysis]
25 Khosla A, Khandnor P, Chand T. A comparative analysis of signal processing and classification methods for different applications based on EEG signals. Biocybernetics and Biomedical Engineering 2020;40:649-90. [DOI: 10.1016/j.bbe.2020.02.002] [Cited by in Crossref: 30] [Cited by in F6Publishing: 36] [Article Influence: 15.0] [Reference Citation Analysis]
26 Bulagang AF, Weng NG, Mountstephens J, Teo J. A review of recent approaches for emotion classification using electrocardiography and electrodermography signals. Informatics in Medicine Unlocked 2020;20:100363. [DOI: 10.1016/j.imu.2020.100363] [Cited by in Crossref: 12] [Cited by in F6Publishing: 5] [Article Influence: 6.0] [Reference Citation Analysis]
27 Huang X, Zhang D, Chen Y, Wang P, Mao C, Miao Z, Liu C, Xu C, Wu X, Yin X. Altered functional connectivity of the red nucleus and substantia nigra in migraine without aura. J Headache Pain 2019;20:104. [PMID: 31711434 DOI: 10.1186/s10194-019-1058-0] [Cited by in Crossref: 11] [Cited by in F6Publishing: 12] [Article Influence: 3.7] [Reference Citation Analysis]