Comportamiento de la mirada y análisis mediante aprendizaje automático de la depresión en la juventud: una revisión sistemática

Derick Axel Lagunes-Ramírez; Gabriel  González-Serna; Leonor Rivera-Rivera; Nimrod González-Franco; Dante Mújica-Vargas; María Y.  Hernández-Pérez

doi:10.29057/xikua.v12i23.11808

Derick Axel Lagunes-Ramírez Departamento de Ciencias Computacionales, TecNM/CENIDET, Cuernavaca, Morelos, México https://orcid.org/0000-0003-0366-6978
Gabriel González-Serna Departamento de Ciencias Computacionales, TecNM/CENIDET, Cuernavaca, Morelos, México https://orcid.org/0000-0002-1874-9402
Leonor Rivera-Rivera Centro de investigaciones en salud poblacional, Instituto Nacional de Salud Pública, Cuernavaca, Morelos, México https://orcid.org/0000-0002-8501-0173
Nimrod González-Franco Departamento de Ciencias Computacionales, TecNM/CENIDET, Cuernavaca, Morelos, México https://orcid.org/0000-0002-1051-1379
Dante Mújica-Vargas Departamento de Ciencias Computacionales, TecNM/CENIDET, Cuernavaca, Morelos, México https://orcid.org/0000-0001-8665-4096
María Y. Hernández-Pérez Departamento de Ciencias Computacionales, TecNM/CENIDET, Cuernavaca, Morelos, Mexico https://orcid.org/0000-0002-8842-0899

DOI: https://doi.org/10.29057/xikua.v12i23.11808

Palabras clave: Depresión, Salud mental, Juventud, Seguimiento ocular, Computación afectiva, Aprendizaje automático

Resumen

Esta revisión tuvo como objetivo comprender la relación entre las características oculares y la depresión en personas jóvenes, para su aplicación en el aprendizaje automático. Se realizó una revisión sistemática para examinar el comportamiento ocular en personas con síntomas depresivos e identificar patrones de movimiento ocular relacionados con trastornos mentales. La búsqueda se realizó utilizando las bases de datos de Google Scholar, Semantic Scholar, PubMed, SpringerLink, MDPI, EBSCO e IEEE Xplore. Se revisaron más de 50 publicaciones de los últimos cinco años. Se revisaron estudios de correlación sobre el comportamiento ocular en personas con depresión y grupos de control, lo que proporcionó información sobre el componente de atención en la depresión. Además, se revisaron investigaciones sobre la detección de la depresión mediante algoritmos de aprendizaje automático y datos oculares donde se utilizaron diferentes paradigmas experimentales de seguimiento ocular y conjuntos de datos para registrar información mientras los participantes observaban estímulos visuales emocionales. Esta revisión presenta relaciones entre diferentes estados mentales y el comportamiento ocular, haciendo hincapié en las poblaciones jóvenes. Mediante la tecnología de seguimiento ocular, es posible apoyar el diagnóstico de la depresión y, por lo tanto, prevenir su desarrollo. Se identificaron tendencias generales para las poblaciones jóvenes y adultas, que deben considerarse en futuras detecciones automáticas de trastornos mentales utilizando datos oculares.

Descargas

La descarga de datos todavía no está disponible.

Citas

. Marcus M, Yasamy MT, Ommeren M, Chisholm D, Saxena S. Depression: A global public health concern. World Heal Organ Pap Depress. 2012 Jan 1;6–8.

. OMS. Depression Facts. 2021.

. Alghowinem S, Goecke R, Wagner M, Parker G, Breakspear M. Eye movement analysis for depression detection. In: 2013 IEEE International Conference on Image Processing [Internet]. IEEE; 2013. p. 4220–4. Available from: http://ieeexplore.ieee.org/document/6738869/

. OMS. Depression and other common mental disorders: global health estimates [Internet]. World Health Organization. Geneva; 2017. p. 1–24. Available from: https://apps.who.int/iris/bitstream/handle/10665/254610/WHO-MSD-MER-2017.2-eng.pdf?sequence=1%0Ahttp://apps.who.int/iris/bitstream/handle/10665/254610/WHO-MSD-MER-2017.2-eng.pdf;jsessionid=0886B5297E6F5A04AA4F2F2FD5FE36F9?sequence=1%0Ahttp://apps.who.int/

. Ng C, How C, Ng Y. Depression in primary care: assessing suicide risk. Singapore Med J [Internet]. 2017 Feb;58(2):72–7. Available from: http://www.smj.org.sg/article/depression-primary-care-assessing-suicide-risk

. OMS. Adolescent mental health [Internet]. 2020. Available from: https://www.who.int/news-room/fact-sheets/detail/adolescent-mental-health

. National Institute of Mental Health (NIMH). Depression [Internet]. 2021. Available from: https://www.nimh.nih.gov/sites/default/files/documents/health/publications/depression/21-mh-8079-depression_0.pdf

. Wu C-T, Huang H-C, Huang S, Chen I-M, Liao S-C, Chen C-K, et al. Resting-State EEG Signal for Major Depressive Disorder Detection: A Systematic Validation on a Large and Diverse Dataset. Biosensors [Internet]. 2021 Dec 6;11(12):499. Available from: https://www.mdpi.com/2079-6374/11/12/499

. Hamilton M. A RATING SCALE FOR DEPRESSION. J Neurol Neurosurg Psychiatry [Internet]. 1960 Feb 1;23(1):56–62. Available from: https://jnnp.bmj.com/lookup/doi/10.1136/jnnp.23.1.56

. Beck AT, Steer RA, Brown GK. BDI-II, Beck Depression Inventory: Manual. San Antonio, TX: The Psychological Corporation; 1996.

. Osman A, Bagge CL, Gutierrez PM, Konick LC, Kopper BA, Barrios FX. The Suicidal Behaviors Questionnaire-Revised (SBQ-R):Validation with Clinical and Nonclinical Samples. Assessment [Internet]. 2001 Dec 26;8(4):443–54. Available from: http://journals.sagepub.com/doi/10.1177/107319110100800409

. Chen S, Epps J, Ruiz N, Chen F. Eye activity as a measure of human mental effort in HCI. In: Proceedings of the 15th international conference on Intelligent user interfaces - IUI ’11 [Internet]. New York, New York, USA: ACM Press; 2011. p. 315. Available from: http://portal.acm.org/citation.cfm?doid=1943403.1943454

. McIntire LK, McKinley RA, Goodyear C, McIntire JP. Detection of vigilance performance using eye blinks. Appl Ergon [Internet]. 2014 Mar;45(2):354–62. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0003687013000938

. Joseph AW, Murugesh R. Potential Eye Tracking Metrics and Indicators to Measure Cognitive Load in Human-Computer Interaction Research. J Sci Res [Internet]. 2020 Jan 1;64(01):168–75. Available from: http://www.bhu.ac.in/research_pub/jsr/Volumes/JSR_64_01_2020/37.pdf

. Lagunes-Ramírez D, Gonzalez-Serna G, Lopez-Sanchez M, Fragoso-Diaz O, Castro-Sanchez N, Olivares-Rojas J. Study of the User’s Eye Tracking to Analyze the Blinking Behavior While Playing a Video Game to Identify Cognitive Load Levels. In: 2020 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC) [Internet]. IEEE; 2020. p. 1–5. Available from: https://ieeexplore.ieee.org/document/9258693/

. Mitre-Hernandez H, Covarrubias-Carrillo R, Lara-Alvarez C. Pupillary Responses for Cognitive Load Measurement: Classifying Difficulty Levels in an Educational Video Game (Preprint). JMIR Serious Games [Internet]. 2020 Jun 19; Available from: http://preprints.jmir.org/preprint/21620/accepted

. Wang Q, Yang S, Liu M, Cao Z, Ma Q. An eye-tracking study of website complexity from cognitive load perspective. Decis Support Syst [Internet]. 2014 Jun;62:1–10. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0167923614000402

. Zagermann J, Pfeil U, Reiterer H. Measuring Cognitive Load using Eye Tracking Technology in Visual Computing. In: Proceedings of the Beyond Time and Errors on Novel Evaluation Methods for Visualization - BELIV ’16 [Internet]. New York, New York, USA: ACM Press; 2016. p. 78–85. Available from: http://dl.acm.org/citation.cfm?doid=2993901.2993908

. Mallick R, Slayback D, Touryan J, Ries AJ, Lance BJ. The use of eye metrics to index cognitive workload in video games. In: 2016 IEEE Second Workshop on Eye Tracking and Visualization (ETVIS) [Internet]. IEEE; 2016. p. 60–4. Available from: http://ieeexplore.ieee.org/document/7851168/

. Zagermann J, Pfeil U, Reiterer H. Studying Eye Movements as a Basis for Measuring Cognitive Load. In: Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems [Internet]. New York, NY, USA: ACM; 2018. p. 1–6. Available from: https://dl.acm.org/doi/10.1145/3170427.3188628

. Gerdes ABM, Alpers GW, Braun H, Köhler S, Nowak U, Treiber L. Emotional sounds guide visual attention to emotional pictures: An eye-tracking study with audio-visual stimuli. Emotion [Internet]. 2020 Mar 19; Available from: http://doi.apa.org/getdoi.cfm?doi=10.1037/emo0000729

. Lagunes-Ramírez DA, Gonzáles-Serna G, Rivera-Rivera L. Patrones de comportamiento ocular durante inducción emocional en diferentes intensidades. Jorn Cienc y Tecnol Apl [Internet]. 2021;4(2):74–9. Available from: https://jcyta.cenidet.tecnm.mx/revistas/jcyta/07-Revista_JCyTA_Vol-4-Num-2_Jul-Dic_2021.pdf

. Lim JZ, Mountstephens J, Teo J. Emotion Recognition Using Eye-Tracking: Taxonomy, Review and Current Challenges. Sensors [Internet]. 2020 Apr 22;20(8):2384. Available from: https://www.mdpi.com/1424-8220/20/8/2384

. Lu Y, Zheng W-L, Li B, Lu B-L. Combining Eye Movements and EEG to Enhance Emotion Recognition. In: Proceedings of the 24th International Conference on Artificial Intelligence [Internet]. AAAI Press; 2015. p. 1170–6. (IJCAI’15). Available from: http://dl.acm.org/citation.cfm?id=2832249.2832411

. Pavlov S V., Korenyok V V., Reva N V., Tumyalis A V., Loktev K V., Aftanas LI. Effects of long-term meditation practice on attentional biases towards emotional faces: An eye-tracking study. Cogn Emot [Internet]. 2015 Jul 4;29(5):807–15. Available from: http://www.tandfonline.com/doi/full/10.1080/02699931.2014.945903

. Skinner IW, Hübscher M, Moseley GL, Lee H, Wand BM, Traeger AC, et al. The reliability of eyetracking to assess attentional bias to threatening words in healthy individuals. Behav Res Methods [Internet]. 2018 Oct 15;50(5):1778–92. Available from: http://link.springer.com/10.3758/s13428-017-0946-y

. Wei-Long Zheng, Bo-Nan Dong, Bao-Liang Lu, Zheng W, Dong B, Lu B, et al. Multimodal emotion recognition using EEG and eye tracking data. In: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society [Internet]. IEEE; 2014. p. 5040–3. Available from: http://ieeexplore.ieee.org/document/6944757/

. Perini G, Cotta Ramusino M, Sinforiani E, Bernini S, Petrachi R, Costa A. Cognitive impairment in depression: recent advances and novel treatments. Neuropsychiatr Dis Treat [Internet]. 2019 May;Volume 15:1249–58. Available from: https://www.dovepress.com/cognitive-impairment-in-depression-recent-advances-and-novel-treatment-peer-reviewed-article-NDT

. Suslow T, Hußlack A, Kersting A, Bodenschatz CM. Attentional biases to emotional information in clinical depression: A systematic and meta-analytic review of eye tracking findings. J Affect Disord [Internet]. 2020 Sep;274:632–42. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0165032720311381

. Zhao Q, Jiao X, Tang Y, Chen S, Tong S, Wang J, et al. Temporal Characteristics of Attentional Disengagement from Emotional Facial Cues in Depression. Neurophysiol Clin [Internet]. 2019 Jun;49(3):235–42. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0987705318302375

. Vazquez C, Blanco I, Sanchez A, McNally RJ. Attentional bias modification in depression through gaze contingencies and regulatory control using a new eye-tracking intervention paradigm: study protocol for a placebo-controlled trial. BMC Psychiatry [Internet]. 2016 Dec 8;16(1):439. Available from: http://bmcpsychiatry.biomedcentral.com/articles/10.1186/s12888-016-1150-9

. Ho H-F. The effects of controlling visual attention to handbags for women in online shops: Evidence from eye movements. Comput Human Behav [Internet]. 2014 Jan;30:146–52. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0747563213003051

. Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS Med [Internet]. 2009 Jul 21;6(7):e1000097. Available from: https://dx.plos.org/10.1371/journal.pmed.1000097

. Methley AM, Campbell S, Chew-Graham C, McNally R, Cheraghi-Sohi S. PICO, PICOS and SPIDER: a comparison study of specificity and sensitivity in three search tools for qualitative systematic reviews. BMC Health Serv Res [Internet]. 2014 Dec 21;14(1):579. Available from: https://bmchealthservres.biomedcentral.com/articles/10.1186/s12913-014-0579-0

. Lu S, Xu J, Li M, Xue J, Lu X, Feng L, et al. Attentional bias scores in patients with depression and effects of age: a controlled, eye-tracking study. J Int Med Res [Internet]. 2017 Oct 29;45(5):1518–27. Available from: http://journals.sagepub.com/doi/10.1177/0300060517708920

. Student. THE PROBABLE ERROR OF A MEAN. Biometrika [Internet]. 1908 Mar 1;6(1):1–25. Available from: https://academic.oup.com/biomet/article-lookup/doi/10.1093/biomet/6.1.1

. Girden ER. ANOVA: Repeated measures. Sage; 1992.

. Burkhouse KL, Siegle GJ, Woody ML, Kudinova AY, Gibb BE. Pupillary reactivity to sad stimuli as a biomarker of depression risk: Evidence from a prospective study of children. J Abnorm Psychol [Internet]. 2015 Aug;124(3):498–506. Available from: http://doi.apa.org/getdoi.cfm?doi=10.1037/abn0000072

. Owens M, Harrison AJ, Burkhouse KL, McGeary JE, Knopik VS, Palmer RHC, et al. Eye tracking indices of attentional bias in children of depressed mothers: Polygenic influences help to clarify previous mixed findings. Dev Psychopathol [Internet]. 2016 May 1;28(2):385–97. Available from: https://www.cambridge.org/core/product/identifier/S0954579415000462/type/journal_article

. Gibb BE, Pollak SD, Hajcak G, Owens M. Attentional biases in children of depressed mothers: An event-related potential (ERP) study. J Abnorm Psychol [Internet]. 2016 Nov;125(8):1166–78. Available from: http://doi.apa.org/getdoi.cfm?doi=10.1037/abn0000216

. Platt B, Sfärlea A, Buhl C, Loechner J, Neumüller J, Asperud Thomsen L, et al. An Eye-Tracking Study of Attention Biases in Children at High Familial Risk for Depression and Their Parents with Depression. Child Psychiatry Hum Dev [Internet]. 2022 Feb 4;53(1):89–108. Available from: http://link.springer.com/10.1007/s10578-020-01105-2

. Greimel E, Piechaczek C, Schulte-Rüther M, Feldmann L, Schulte-Körne G. The role of attentional deployment during distancing in adolescents with major depression. Behav Res Ther [Internet]. 2020 Mar;126:103554. Available from: https://linkinghub.elsevier.com/retrieve/pii/S000579672030005X

. Garcia SE, Francis SMS, Tone EB, Tully EC. Understanding associations between negatively biased attention and depression and social anxiety: positively biased attention is key. Anxiety, Stress Coping [Internet]. 2019 Nov 2;32(6):611–25. Available from: https://www.tandfonline.com/doi/full/10.1080/10615806.2019.1638732

. Burkhouse KL, Owens M, Feurer C, Sosoo E, Kudinova A, Gibb BE. Increased neural and pupillary reactivity to emotional faces in adolescents with current and remitted major depressive disorder. Soc Cogn Affect Neurosci [Internet]. 2017 May 1;12(5):783–92. Available from: https://academic.oup.com/scan/article/12/5/783/2726393

. Posner MI. Orienting of Attention. Q J Exp Psychol. 1980 Feb;32(1):3–25.

. Sanchez A, Romero N, De Raedt R. Depression-related difficulties disengaging from negative faces are associated with sustained attention to negative feedback during social evaluation and predict stress recovery. Allen P, editor. PLoS One [Internet]. 2017 Mar 31;12(3):e0175040. Available from: https://dx.plos.org/10.1371/journal.pone.0175040

. Jin AB, Steding LH, Webb AK. Reduced emotional and cardiovascular reactivity to emotionally evocative stimuli in major depressive disorder. Int J Psychophysiol [Internet]. 2015 Jul;97(1):66–74. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0167876015001622

. Klawohn J, Bruchnak A, Burani K, Meyer A, Lazarov A, Bar-Haim Y, et al. Aberrant attentional bias to sad faces in depression and the role of stressful life events: Evidence from an eye-tracking paradigm. Behav Res Ther [Internet]. 2020 Dec;135:103762. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0005796720302163

. Krejtz I, Holas P, Rusanowska M, Nezlek JB. Positive online attentional training as a means of modifying attentional and interpretational biases among the clinically depressed: An experimental study using eye tracking. J Clin Psychol [Internet]. 2018 Sep;74(9):1594–606. Available from: http://doi.wiley.com/10.1002/jclp.22617

. Bodenschatz CM, Skopinceva M, Ruß T, Suslow T. Attentional bias and childhood maltreatment in clinical depression - An eye-tracking study. J Psychiatr Res [Internet]. 2019 May;112:83–8. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0022395618313232

. Figueiredo GR, Ripka WL, Romaneli EFR, Ulbricht L. Attentional bias for emotional faces in depressed and non-depressed individuals: an eye-tracking study. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) [Internet]. IEEE; 2019. p. 5419–22. Available from: https://ieeexplore.ieee.org/document/8857878/

. Tang W, Bao C, Xu L, Zhu J, Feng W, Zhang W, et al. Depressive Symptoms in Late Pregnancy Disrupt Attentional Processing of Negative–Positive Emotion: An Eye-Movement Study. Front Psychiatry [Internet]. 2019 Oct 31;10. Available from: https://www.frontiersin.org/article/10.3389/fpsyt.2019.00780/full

. Yaroslavsky I, Allard ES, Sanchez-Lopez A. Can’t look Away: Attention control deficits predict Rumination, depression symptoms and depressive affect in daily Life. J Affect Disord [Internet]. 2019 Feb;245:1061–9. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0165032718314150

. Bodenschatz CM, Skopinceva M, Ruß T, Kersting A, Suslow T. Face perception without subjective awareness – Emotional expressions guide early gaze behavior in clinically depressed and healthy individuals. J Affect Disord [Internet]. 2020 Mar;265:91–8. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0165032719315885

. Unruh KE, Bodfish JW, Gotham KO. Adults with Autism and Adults with Depression Show Similar Attentional Biases to Social-Affective Images. J Autism Dev Disord [Internet]. 2020 Jul 7;50(7):2336–47. Available from: http://link.springer.com/10.1007/s10803-018-3627-5

. Godara M, Sanchez-Lopez A, De Raedt R. Music to my ears, goal for my eyes? Music reward modulates gaze disengagement from negative stimuli in dysphoria. Behav Res Ther [Internet]. 2019 Sep;120:103434. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0005796719301202

. MacLeod C, Mathews A, Tata P. Attentional bias in emotional disorders. J Abnorm Psychol [Internet]. 1986;95(1):15–20. Available from: http://doi.apa.org/getdoi.cfm?doi=10.1037/0021-843X.95.1.15

. Lang PJ, Bradley MM, Uthbert BN. International affective picture system (IAPS): Affective ratings of pictures and instruction manual. Gainesville, Florida.; 2008.

. Stolicyn A, Steele JD, Seriès P. Prediction of depression symptoms in individual subjects with face and eye movement tracking. Psychol Med [Internet]. 2020 Nov 9;1–9. Available from: https://www.cambridge.org/core/product/identifier/S0033291720003608/type/journal_article

. Zhu J, Wang Z, Gong T, Zeng S, Li X, Hu B, et al. An Improved Classification Model for Depression Detection Using EEG and Eye Tracking Data. IEEE Trans Nanobioscience [Internet]. 2020 Jul;19(3):527–37. Available from: https://ieeexplore.ieee.org/document/9079496/

. Zhu J, Wang Z, Zeng S, Li X, Hu B, Zhang X, et al. Toward Depression Recognition Using EEG and Eye Tracking: An Ensemble Classification Model CBEM. In: 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) [Internet]. IEEE; 2019. p. 782–6. Available from: https://ieeexplore.ieee.org/document/8983225/

. Alghowinem S, Goecke R, Wagner M, Epps J, Hyett M, Parker G, et al. Multimodal Depression Detection: Fusion Analysis of Paralinguistic, Head Pose and Eye Gaze Behaviors. IEEE Trans Affect Comput [Internet]. 2018 Oct 1;9(4):478–90. Available from: https://ieeexplore.ieee.org/document/7763752/

. Shen R, Zhan Q, Wang Y, Ma H. Depression Detection by Analysing Eye Movements on Emotional Images. In: ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) [Internet]. IEEE; 2021. p. 7973–7. Available from: https://ieeexplore.ieee.org/document/9414663/

. Pan Z, Ma H, Zhang L, Wang Y. Depression Detection Based on Reaction Time and Eye Movement. In: 2019 IEEE International Conference on Image Processing (ICIP) [Internet]. IEEE; 2019. p. 2184–8. Available from: https://ieeexplore.ieee.org/document/8803181/

. Wang H, Zhou Y, Yu F, Zhao L, Wang C, Ren Y. Fusional Recognition for Depressive Tendency With Multi-Modal Feature. IEEE Access [Internet]. 2019;7:38702–13. Available from: https://ieeexplore.ieee.org/document/8667078/

. Li M, Cao L, Zhai Q, Li P, Liu S, Li R, et al. Method of Depression Classification Based on Behavioral and Physiological Signals of Eye Movement. Complexity [Internet]. 2020 Jan 14;2020:1–9. Available from: https://www.hindawi.com/journals/complexity/2020/4174857/

. Shafiei SB, Lone Z, Elsayed AS, Hussein AA, Guru KA. Identifying mental health status using deep neural network trained by visual metrics. Transl Psychiatry [Internet]. 2020 Dec 14;10(1):430. Available from: http://www.nature.com/articles/s41398-020-01117-5

. Al-gawwam S, Benaissa M. Depression Detection From Eye Blink Features. In: 2018 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) [Internet]. IEEE; 2018. p. 388–92. Available from: https://ieeexplore.ieee.org/document/8642682/

. Lu S, Liu S, Li M, Shi X, Li R. Depression Classification Model Based on Emotionally Related Eye-Movement Data and Kernel Extreme Learning Machine. J Med Imaging Heal Informatics [Internet]. 2020 Nov 1;10(11):2668–74. Available from: https://www.ingentaconnect.com/content/10.1166/jmihi.2020.3198

. Gavrilescu M, Vizireanu N. Predicting Depression, Anxiety, and Stress Levels from Videos Using the Facial Action Coding System. Sensors [Internet]. 2019 Aug 25;19(17):3693. Available from: https://www.mdpi.com/1424-8220/19/17/3693

. Yuan Y, Wang Q. Detection Model of Depression Based on Eye Movement Trajectory. In: 2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA) [Internet]. IEEE; 2019. p. 612–3. Available from: https://ieeexplore.ieee.org/document/8964128/

. Zhu J, Wang Y, La R, Zhan J, Niu J, Zeng S, et al. Multimodal Mild Depression Recognition Based on EEG-EM Synchronization Acquisition Network. IEEE Access [Internet]. 2019;7:28196–210. Available from: https://ieeexplore.ieee.org/document/8653893/

. 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. J Affect Disord [Internet]. 2019 May;251:156–61. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0165032718330064

. Li X, Cao T, Sun S, Hu B, Ratcliffe M. Classification study on eye movement data: Towards a new approach in depression detection. In: 2016 IEEE Congress on Evolutionary Computation (CEC) [Internet]. IEEE; 2016. p. 1227–32. Available from: http://ieeexplore.ieee.org/document/7743927/

. Parkitny L, McAuley J. The Depression Anxiety Stress Scale (DASS). J Physiother [Internet]. 2010;56(3):204. Available from: https://linkinghub.elsevier.com/retrieve/pii/S1836955310700308

. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: Machine Learning in {P}ython. J Mach Learn Res. 2011;12:2825–30.