Automated diagnosis of depression from EEG signals using traditional and deep learning approaches: A comparative analysis

• Autorzy: Khosla Ashima , Khandnor Padmavati , Chand Trilok

Identyfikatory

DOI

10.1016/j.bbe.2021.12.005

• Języki publikacji: EN

Abstrakty

EN

Depression is one of the significant contributors to the global burden disease, affecting nearly 264 million people worldwide along with the increasing rate of suicidal deaths. Electroencephalogram (EEG), a non-invasive functional neuroimaging tool has been widely used to study the significant biomarkers for the diagnosis of the disorder. Computational Psychiatry is a novel avenue of research that has shown a tremendous success in the automated diagnosis of depression. The present comprehensive review concentrate on two approaches widely adopted for an EEG based automated diagnosis of depression: Deep Learning (DL) approach and the traditional approach based upon Machine Learning (ML). In this review, we focus on performing the comparative analysis of a variety of signal processing and classification methods adopted in the existing literature for these approaches. We have discussed a variety of EEG based objective biomarkers and the data acquisition systems adopted for the diagnosis of depression. Few EEG studies focusing on multimodal fusion of data have also been explained. Additionally, the research based upon the analysis and prediction of treatment outcome response for depression using EEG signals and machine learning techniques has been briefly discussed to aware the researchers about this emerging field. Finally, the future opportunities and a valuable discussion on major issues related to this field have been summarized that will help the researchers in developing more reliable and computationally intelligent systems in the field of psychiatry.

Słowa kluczowe

EN

electroencephalogram; major depressive disorder; automated diagnosis; treatment outcome prediction; machine learning; deep learning

PL

elektroencefalogram; depresja; diagnoza automatyczna; uczenie maszynowe; uczenie głębokie

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2022
• Tom: Vol. 42, no. 1
• Strony: 108--142
• Opis fizyczny: Bibliogr. 132 poz., rys., tab.

Twórcy

autor

Khosla Ashima

• Computer Science and Engineering Department, Chandigarh, India; Punjab Engineering College (Deemed to be University), Chandigarh, India

autor

Khandnor Padmavati

• Computer Science and Engineering Department, Chandigarh, India; Punjab Engineering College (Deemed to be University), Chandigarh, India

autor

Chand Trilok

• Computer Science and Engineering Department, Chandigarh, India; Punjab Engineering College (Deemed to be University), Chandigarh, India

Bibliografia

• [1] James SL, Abate D, Abate KH, Abay SM, Abbafati C, Abbasi N, et al. Global, regional, and national incidence, prevalence, and years lived with disability for 354 Diseases and Injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 2018;392(10159):1789–858. https://doi.org/10.1016/S0140- 6736(18)32279-7.
• [2] de Kwaasteniet B, Ruhe E, Caan M, Rive M, Olabarriaga S, Groefsema M, et al. Relation between structural and functional connectivity in major depressive disorder. Biol Psychiatry 2013;74(1):40–7. https://doi.org/10.1016/j. Biopsych.2012.12.024.
• [3] Vederine F-E, Wessa M, Leboyer M, Houenou J. A metaanalysis of whole-brain diffusion tensor imaging studies in bipolar disorder. Prog Neuro-Psychopharmacology Biol Psychiatry 2011;35(8):1820–6. https://doi.org/10.1016/j. Pnpbp.2011.05.009.
• [4] Du L, Liu H, Du W, Chao F, Zhang L, Wang K, et al. Stimulated left DLPFC-nucleus accumbens functional connectivity predicts the anti-depression and anti-anxiety effects of rTMS for depression. Transl Psychiatry 2017;7(11). https:// doi.org/10.1038/s41398-017-0005-6.
• [5] del Barrio V. Diagnostic and Statistical Manual of Mental Disorders. 2004. doi: 10.1016/B0-12-657410-3/00457-8.
• [6] Beck AT, Ward CH, Mendelson M, Mock J, Erbaugh J. An inventory for measuring depression. Arch Gen Psychiatry 1961;4:561–71. https://doi.org/10.1001/ archpsyc.1961.01710120031004.
• [7] Jollans L, Whelan R. The clinical added value of imaging: a perspective from outcome prediction. Biol Psychiatry Cogn Neurosci Neuroimaging 2016;1(5):423–32. https://doi.org/ 10.1016/j.bpsc.2016.04.005.
• [8] Gillan CM, Whelan R. What big data can do for treatment in psychiatry. Curr Opin Behav Sci 2017;18:34–42. https://doi. org/10.1016/j.cobeha.2017.07.003.
• [9] Gillan C, Daw N. Taking psychiatry research online. Neuron 2016;91(1):19–23. https://doi.org/10.1016/j.neuron.2016.06.002.
• [10] Kim D-J, Bolbecker AR, Howell J, Rass O, Sporns O, Hetrick WP, et al. Disturbed resting state EEG synchronization in bipolar disorder: a graph-theoretic analysis. NeuroImage Clin 2013;2:414–23. https://doi.org/10.1016/j.nicl.2013.03.007.
• [11] Lee T-W, Wu Y-T, Yu Y-Y, Chen M-C, Chen T-J. The implication of functional connectivity strength in predicting treatment response of major depressive disorder: a resting EEG study. Psychiatry Res – Neuroimaging 2011;194(3):372–7. https://doi.org/10.1016/j.pscychresns.2011.02.009.
• [12] Pinto MF, Leal A, Lopes F, Dourado A, Martins P, Teixeira CA. A personalized and evolutionary algorithm for interpretable EEG epilepsy seizure prediction. Sci Rep 2021;11:1–12. https://doi.org/10.1038/s41598-021-82828-7.
• [13] Olbrich S, Arns M. EEG biomarkers in major depressive disorder: Discriminative power and prediction of treatment response. Int Rev Psychiatry 2013;25(5):604–18. https://doi. Org/10.3109/09540261.2013.816269.
• [14] Bachmann M, Lass J, Suhhova A, Hinrikus H. Spectral asymmetry and Higuchi’s fractal dimension measures of depression electroencephalogram. Comput Math Methods Med 2013;2013:1–8. https://doi.org/10.1155/2013/251638.
• [15] Cukic M, Pokrajac D, Stokic M, Simic slobodan, Radivojevic V, Ljubisavljevic M. EEG machine learning with Higuchi fractal dimension and Sample Entropy as features for successful detection of depression 2018:1–34. doi: 10.1007/ s11571-020-09581-x.
• [16] Torre Luque A de la, Bornas X. Complexity and Irregularity in the Brain Oscillations of Depressive Patients: A Systematic Review. Neuropsychiatry (London) 2017;07. doi: 10.4172/neuropsychiatry.1000238.
• [17] Ahmadlou M, Adeli H, Adeli A. Fractality analysis of frontal brain in major depressive disorder. Int J Psychophysiol 2012;85(2):206–11. https://doi.org/10.1016/j. Ijpsycho.2012.05.001.
• [18] Hosseinifard B, Moradi MH, Rostami R. Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal. Comput Methods Programs Biomed 2013;109(3):339–45. https://doi.org/10.1016/j.cmpb.2012.10.008.
• [19] Čukić M, Stokić M, Radenković S, Ljubisavljević M, Simić S, Savić D. Nonlinear analysis of EEG complexity in episode and remission phase of recurrent depression. Int J Methods Psychiatr Res 2020;29:1–11. https://doi.org/10.1002/ mpr.1816.
• [20] Jollans L, Whelan R. Neuromarkers for mental disorders: harnessing population neuroscience. Front Psychiatry 2018;9:1–14. https://doi.org/10.3389/fpsyt.2018.00242.
• [21] Huys QJM, Maia TV, Frank MJ. Computational psychiatry as a bridge from neuroscience to clinical applications. Nat Neurosci 2016;19(3):404–13. https://doi.org/10.1038/nn.4238.
• [22] Kato A, Kunisato Y, Katahira K, Okimura T, Yamashita Y. Computational Psychiatry Research Map (CPSYMAP): A New Database for Visualizing Research Papers. Front Psychiatry 2020;11:1–9. https://doi.org/10.3389/fpsyt.2020.578706.
• [23] Crosson B, Ford A, McGregor KM, Meinzer M, Cheshkov S, Li X, et al. Functional imaging and related techniques: An introduction for rehabilitation researchers. J Rehabil Res Dev 2010;47(2):vii. https://doi.org/10.1682/JRRD.2010.02.0017.
• [24] Babiloni C, Pizzella V, Del GC, Ferretti A, Romani GL. Chapter 5 Fundamentals of Electroencefalography, Magnetoencefalography, and Functional Magnetic Resonance Imaging 2009;vol. 86. https://doi.org/10.1016/S0074-7742(09)86005-4.
• [25] Rabcan J, Levashenko V, Zaitseva E, Kvassay M. Review of methods for EEG signal classification and development of new fuzzy classification-based approach. IEEE Access 2020;8:189720–34. https://doi.org/10.1109/ Access.628763910.1109/ACCESS.2020.3031447.
• [26] Cao J, Grajcar K, Shan X, Zhao Y, Zou J, Chen L, et al. Using interictal seizure-free EEG data to recognise patients with epilepsy based on machine learning of brain functional connectivity. Biomed Signal Process Control 2021;67:102554. https://doi.org/10.1016/j.bspc.2021.102554.
• [27] Bavkar S, Iyer B, Deosarkar S. Optimal EEG channels selection for alcoholism screening using EMD domain statistical features and harmony search algorithm. Biocybern Biomed Eng 2021;41(1):83–96. https://doi.org/ 10.1016/j.bbe.2020.11.001.
• [28] Gaubert S, Houot M, Raimondo F, Ansart M, Corsi M-C, Naccache L, et al. A machine learning approach to screen for preclinical Alzheimer’s disease. Neurobiol Aging 2021;105:205–16. https://doi.org/10.1016/j.neurobiolaging.2021.04.024.
• [29] Khare SK, Bajaj V, Acharya UR. PDCNNet: an automatic framework for the detection of Parkinson’s disease using EEG signals. IEEE Sens J 2021;21(15):17017–24. https://doi.org/ 10.1109/JSEN.2021.3080135.
• [30] Eldele E, Chen Z, Liu C, Wu M, Kwoh C-K, Li X, et al. An attention-based deep learning approach for sleep stage classification with single-channel EEG. IEEE Trans Neural Syst Rehabil Eng 2021;29:809–18. https://doi.org/10.1109/ TNSRE.2021.3076234.
• [31] Vázquez MA, Maghsoudi A, Mariño IP. An interpretable machine learning method for the detection of schizophrenia using EEG signals. Front Syst Neurosci 2021;15:1–11. https://doi.org/10.3389/fnsys.2021.652662.
• [32] Radenković MČ , Lopez VL. Machine Learning Approaches for Detecting the Depression from Resting-State Electroencephalogram (EEG): A Review Study 2019:1–31.
• [33] Čukić M, López V, Pavón J. Classification of depression through resting-state electroencephalogram as a novel practice in psychiatry: review. J Med Internet Res 2020;22: e19548. https://doi.org/10.2196/19548.
• [34] Bolhasani H. Depression diagnosis by deep learning using EEG signals : A Systematic Review 2021. doi: 10.20944/ preprints202107.0028.v1.
• [35] Yasin S, Hussain SA, Aslan S, Raza I, Muzammel M, Othmani A. EEG based major depressive disorder and bipolar disorder detection using neural networks: a review. Comput Methods Programs Biomed 2021;202:106007. https://doi.org/10.1016/j.cmpb.2021.106007.
• [36] Acharya UR, Sudarshan VK, Adeli H, Santhosh J, Koh JEW, Adeli A. Computer-aided diagnosis of depression using EEG signals. Eur Neurol 2015;73(5-6):329–36. https://doi.org/ 10.1159/000381950.
• [37] Keren H, O’Callaghan G, Vidal-Ribas P, Buzzell GA, Brotman MA, Leibenluft E, et al. Reward processing in depression: A conceptual and meta-analytic review across fMRI and EEG studies. Am J Psychiatry 2018;175(11):1111–20. https://doi. org/10.1176/appi.ajp.2018.17101124.
• [38] de Aguiar Neto FS, Rosa JLG. Depression biomarkers using non-invasive EEG: a review. Neurosci Biobehav Rev 2019;105:83–93. https://doi.org/10.1016/j. Neubiorev.2019.07.021.
• [39] Knociková JA, Petrá sek T. Quantitative electroencephalographic biomarkers behind major depressive disorder. Biomed Signal Process Control 2021;68:102596. https://doi.org/10.1016/j.bspc.2021.102596.
• [40] de Freitas SB, Marques AA, Bevilaqua MC, de Carvalho MR, Ribeiro P, Palmer S, et al. Electroencephalographic findings in patients with major depressive disorder during cognitive or emotional tasks: a systematic review. Rev Bras Psiquiatr 2016;38(4):338–46. https://doi.org/10.1590/1516-4446-2015- 1834.
• [41] Khosla A, Khandnor P, Chand T. A comparative analysis of signal processing and classification methods for different applications based on EEG signals. Biocybern Biomed Eng 2020;40(2):649–90. https://doi.org/10.1016/j.bbe.2020.02.002.
• [42] Goodfellow I, Bengio, Yoshua, Courville Aaron. Deep Learning Ian. Foreign Aff 2012;91:1689–99.
• [43] Rashid M, Singh H, Goyal V. The use of machine learning and deep learning algorithms in functional magnetic resonance imaging—a systematic review. Expert Syst 2020;37:1–29. https://doi.org/10.1111/exsy.12644.
• [44] Čukić M, Stokić M, Simić S, Pokrajac D. The successful discrimination of depression from EEG could be attributed to proper feature extraction and not to a particular classification method. Cogn Neurodyn 2020;14(4):443–55. https://doi.org/10.1007/s11571-020-09581-x.
• [45] Sengupta S, Basak S, Saikia P, Paul S, Tsalavoutis V, Atiah F, et al. A review of deep learning with special emphasis on architectures, applications and recent trends. Knowledge-Based Syst 2020;194:105596. https://doi.org/10.1016/j. Knosys.2020.105596.
• [46] Čukić M, Pokrajac D, Lopez V. On mistakes we made in prior computational psychiatry data driven approach projects and how they jeopardize translation of those findings in clinical practice. Adv Intell Syst Comput 2021;1252 AISC:493–510. Doi: 10.1007/978-3-030-55190-2_37.
• [47] Lebiecka K, Zuchowicz U, Wozniak-Kwasniewska A, Szekely D, Olejarczyk E, David O. Complexity analysis of eeg data in persons with depression subjected to transcranial magnetic stimulation. Front Physiol 2018;9:1–11. https://doi.org/ 10.3389/fphys.2018.01385.
• [48] Zuchowicz U, Wozniak-Kwasniewska A, Szekely D, Olejarczyk E, David O. EEG phase synchronization in persons with depression subjected to transcranial magnetic stimulation. Front Neurosci 2019;13:1–17. https://doi.org/ 10.3389/fnins.2018.01037.
• [49] Liu W, Zhang C, Wang X, Xu J, Chang Yi, Ristaniemi T, et al. Functional connectivity of major depression disorder using ongoing EEG during music perception. Clin Neurophysiol 2020;131(10):2413–22. https://doi.org/10.1016/j. Clinph.2020.06.031.
• [50] Mumtaz W, Ali SSA, Yasin MAM, Malik AS. A machine learning framework involving EEG-based functional connectivity to diagnose major depressive disorder (MDD). Med Biol Eng Comput 2018;56(2):233–46. https://doi.org/ 10.1007/s11517-017-1685-z.
• [51] Wan Z, Huang J, Zhang H, Zhou H, Yang J, Zhong N. HybridEEGNet: a convolutional neural network for EEG feature learning and depression discrimination. IEEE Access 2020;8:30332–42. https://doi.org/10.1109/ Access.628763910.1109/ACCESS.2020.2971656.
• [52] Fingelkurts AA, Fingelkurts AA. Altered structure of dynamic electroencephalogram oscillatory pattern in major depression. Biol Psychiatry 2015;77(12):1050–60. https://doi. org/10.1016/j.biopsych.2014.12.011.
• [53] Gollan JK, Hoxha D, Chihade D, Pflieger ME, Rosebrock L, Cacioppo J. Frontal alpha EEG asymmetry before and after behavioral activation treatment for depression. Biol Psychol 2014;99:198–208. https://doi.org/10.1016/j. Biopsycho.2014.03.003.
• [54] Arns M, Bruder G, Hegerl U, Spooner C, Palmer DM, Etkin A, et al. EEG alpha asymmetry as a gender-specific predictor of outcome to acute treatment with different antidepressant medications in the randomized iSPOT-D study. Clin Neurophysiol 2016;127(1):509–19. https://doi.org/10.1016/j. Clinph.2015.05.032.
• [55] van der Vinne N, Vollebregt MA, van Putten MJAM, Arns M. Frontal alpha asymmetry as a diagnostic marker in depression: Fact or fiction? A meta-analysis. NeuroImage Clin 2017;16:79–87. https://doi.org/10.1016/j.nicl.2017.07.006.
• [56] Kaiser AK, Doppelmayr M, Iglseder B. Alpha-Asymmetrie im Elektroenzephalogramm bei geriatrischer Depression: Valide oder verschwunden?. Z Gerontol Geriatr 2018;51:200–5. https://doi.org/10.1007/s00391-016-1108-z.
• [57] van der Vinne N, Vollebregt MA, van Putten MJAM, Arns M. Stability of frontal alpha asymmetry in depressed patients during antidepressant treatment. NeuroImage Clin 2019;24:102056. https://doi.org/10.1016/j.nicl.2019.102056.
• [58] Duan L, Duan H, Qiao Y, Sha S, Qi S, Zhang X, et al. Machine learning approaches for MDD detection and emotion decoding using EEG signals. Front Hum Neurosci 2020;14. https://doi.org/10.3389/fnhum.2020.00284.
• [59] Mahato S, Paul S. Classification of depression patients and normal subjects based on electroencephalogram (EEG) signal using alpha power and theta asymmetry. J Med Syst 2020;44(1). https://doi.org/10.1007/s10916-019-1486-z.
• [60] Baskaran A, Farzan F, Milev R, Brenner CA, Alturi S, Pat McAndrews M, et al. The comparative effectiveness of electroencephalographic indices in predicting response to escitalopram therapy in depression: a pilot study. J Affect Disord 2018;227:542–9. https://doi.org/10.1016/j. Jad.2017.10.028.
• [61] Bachmann M, Päeske L, Kalev K, Aarma K, Lehtmets A, Ööpik P, et al. Methods for classifying depression in single channel EEG using linear and nonlinear signal analysis. Comput Methods Programs Biomed 2018;155:11–7. https:// doi.org/10.1016/j.cmpb.2017.11.023.
• [62] Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adeli H, Subha DP. Automated EEG-based screening of depression using deep convolutional neural network. Comput Methods Programs Biomed 2018;161:103–13. https://doi.org/10.1016/j. Cmpb.2018.04.012.
• [63] Sharma M, Achuth PV, Deb D, Puthankattil SD, Acharya UR. An automated diagnosis of depression using three-channel bandwidth-duration localized wavelet filter bank with EEG signals. Cogn Syst Res 2018;52:508–20. https://doi.org/ 10.1016/j.cogsys.2018.07.010.
• [64] 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 2019;251:156–61. https://doi.org/10.1016/j.jad.2019.03.058.
• [65] Mumtaz W, Xia L, Mohd Yasin MA, Azhar Ali SS, Malik AS, Hu D. A wavelet-based technique to predict treatment outcome for Major Depressive Disorder. PLoS ONE 2017;12 (2):e0171409. https://doi.org/10.1371/journal.pone.0171409.
• [66] Mumtaz, Wajid (2016): MDD Patients and Healthy Controls EEG Data (New). figshare. Dataset. doi: 10.6084/m9. figshare.4244171.v2.
• [67] Sharma G, Parashar A, Joshi AM. DepHNN: a novel hybrid neural network for electroencephalogram (EEG)-based screening of depression. Biomed Signal Process Control 2021;66:102393. https://doi.org/10.1016/j.bspc.2020.102393.
• [68] Cavanagh JF, Bismark AJ, Frank MJ, Allen JJB. Larger error signals in major depression are associated with better avoidance learning. Front Psychol 2011;2:1–6. https://doi. org/10.3389/fpsyg.2011.00331.
• [69] Saeedi A, Saeedi M, Maghsoudi A, Shalbaf A. Major depressive disorder diagnosis based on effective connectivity in EEG signals: a convolutional neural network and long short-term memory approach. Cogn Neurodyn 2021;15(2):239–52. https://doi.org/10.1007/s11571-020-09619-0.
• [70] Cai H, Chen Y, Han J, Zhang X, Hu B. Study on feature selection methods for depression detection using three-electrode EEG data. Interdiscip Sci Comput Life Sci 2018;10 (3):558–65. https://doi.org/10.1007/s12539-018-0292-5.
• [71] Mahato S, Paul S. Detection of major depressive disorder using linear and non-linear features from EEG signals. Microsyst Technol 2019;25(3):1065–76. https://doi.org/10.1007/s00542-018-4075-z.
• [72] Mahato S, Goyal N, Ram D, Paul S. Detection of depression and scaling of severity using six channel EEG data. J Med Syst 2020;44(7). https://doi.org/10.1007/s10916-020-01573-y.
• [73] Li X, La R, Wang Y, Niu J, Zeng S, Sun S, et al. EEG-based mild depression recognition using convolutional neural network. Med Biol Eng Comput 2019;57(6):1341–52. https://doi.org/ 10.1007/s11517-019-01959-2.
• [74] Ay B, Yildirim O, Talo M, Baloglu UB, Aydin G, Puthankattil SD, et al. Automated depression detection using deep representation and sequence learning with EEG signals. J Med Syst 2019;43(7). https://doi.org/10.1007/s10916-019-1345-y.
• [75] Saeedi M, Saeedi A, Maghsoudi A. Major depressive disorder assessment via enhanced k-nearest neighbor method and EEG signals. Phys Eng Sci Med 2020;43(3):1007–18. https:// doi.org/10.1007/s13246-020-00897-w.
• [76] Cai H, Han J, Chen Y, Sha X, Wang Z, Hu B, et al. A pervasive approach to EEG-based depression detection. Complexity 2018;2018:1–13. https://doi.org/10.1155/2018/5238028.
• [77] Thoduparambil PP, Dominic A, Varghese SM. EEG-based deep learning model for the automatic detection of clinical depression. Phys Eng Sci Med 2020;43(4):1349–60. https://doi. org/10.1007/s13246-020-00938-4.
• [78] Cavanagh JF, Bismark AW, Frank MJ, Allen JJB. Multiple dissociations between comorbid depression and anxiety on reward and punishment processing: evidence from computationally informed EEG. Comput Psychiatry 2019;3:1. https://doi.org/10.1162/cpsy_a_00024.
• [79] Akbari H, Sadiq MT, Payan M, Esmaili SS, Baghri H, Bagheri H. Depression detection based on geometrical features extracted from sodp shape of EEG signals and binary PSO. Trait Du Signal 2021;38(1):13–26.
• [80] Akbari H, Sadiq MT, Ur Rehman A, Ghazvini M, Naqvi RA, Payan M, et al. Depression recognition based on the reconstruction of phase space of EEG signals and geometrical features. Appl Acoust 2021;179:108078. https:// doi.org/10.1016/j.apacoust.2021.108078.
• [81] Li Y, Hu B, Zheng X, Li X. EEG-based mild depressive detection using differential evolution. IEEE Access 2019;7:7814–22. https://doi.org/10.1109/ ACCESS.2018.2883480.
• [82] Peng H, Xia C, Wang Z, Zhu J, Zhang X, Sun S, et al. Multivariate pattern analysis of eeg-based functional connectivity: a study on the identification of depression. IEEE Access 2019;7:92630–41. https://doi.org/10.1109/ Access.628763910.1109/ACCESS.2019.2927121.
• [83] Seal A, Bajpai R, Agnihotri J, Yazidi A, Herrera-Viedma E, Krejcar O. A deep convolution neural network framework for detecting depression using EEG. IEEE Trans Instrum Meas 2021;70:1–13. https://doi.org/10.1109/TIM.1910.1109/ TIM.2021.3053999.
• [84] Cai H, Qu Z, Li Z, Zhang Y, Hu X, Hu B. Feature-level fusion approaches based on multimodal EEG data for depression recognition. Inf Fusion 2020;59:127–38. https://doi.org/ 10.1016/j.inffus.2020.01.008.
• [85] 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 Nanobiosci 2020;19(3):527–37. https://doi.org/10.1109/TNB.772810.1109/TNB.2020.2990690.
• [86] Aydemir E, Tuncer T, Dogan S, Gururajan R, Acharya UR. Automated major depressive disorder detection using melamine pattern with EEG signals. Appl Intell 2021;51 (9):6449–66. https://doi.org/10.1007/s10489-021-02426-y.
• [87] Movahed RA, Jahromi GP, Shahyad S, Meftahi GH. A major depressive disorder classification framework based on EEG signals using statistical, spectral, wavelet, functional connectivity, and nonlinear analysis. J Neurosci Methods 2021;358:109209. https://doi.org/10.1016/j. Jneumeth.2021.109209.
• [88] Li X, La R, Wang Y, Hu B, Zhang X. A deep learning approach for mild depression recognition based on functional connectivity using electroencephalography. Front Neurosci 2020;14:1–20. https://doi.org/10.3389/fnins.2020.00192.
• [89] Mumtaz W, Qayyum A. A deep learning framework for automatic diagnosis of unipolar depression. Int J Med Inform 2019;132:103983. https://doi.org/10.1016/j. Ijmedinf.2019.103983.
• [90] Kaur C, Bisht A, Singh P, Joshi G. EEG signal denoising using hybrid approach of variational mode decomposition and wavelets for depression. Biomed Signal Process Control 2021;65:102337. https://doi.org/10.1016/j.bspc.2020.102337.
• [91] Li X, Zhang X, Zhu J, Mao W, Sun S, Wang Z, et al. Depression recognition using machine learning methods with different feature generation strategies. Artif Intell Med 2019;99:101696. https://doi.org/10.1016/j.artmed.2019.07.004.
• [92] Yeung N, Bogacz R, Holroyd CB, Cohen JD. Detection of synchronized oscillations in the electroencephalogram: an evaluation of methods. Psychophysiology 2004;41(6):822–32. https://doi.org/10.1111/psyp.2004.41.issue-610.1111/j.1469- 8986.2004.00239.x.
• [93] Yeung N, Bogacz R, Holroyd CB, Nieuwenhuis S, Cohen JD. Theta phase resetting and the error-related negativity. Psychophysiology 2007;44:39–49. https://doi.org/10.1111/ j.1469-8986.2006.00482.x.
• [94] Kang M, Kwon H, Park JH, Kang S, Lee Y. Deep-asymmetry: asymmetry matrix image for deep learning method in prescreening depression. Sensors (Switzerland) 2020;20:1–12. https://doi.org/10.3390/s20226526.
• [95] Khan DM, Yahya N, Kamel N, Faye I. Automated diagnosis of major depressive disorder using brain effective connectivity and 3D convolutional neural network. IEEE Access 2021;9:8835–46. https://doi.org/10.1109/ Access.628763910.1109/ACCESS.2021.3049427.
• [96] Uyulan C, Ergüzel TT, Unubol H, Cebi M, Sayar GH, Nezhad Asad M, et al. Major depressive disorder classification based on different convolutional neural network models: deep learning approach. Clin EEG Neurosci 2021;52(1):38–51. https://doi.org/10.1177/1550059420916634.
• [97] Jiang C, Li Y, Tang Y, Guan C. Enhancing EEG-based classification of depression patients using spatial information. IEEE Trans Neural Syst Rehabil Eng 2021;29:566–75. https://doi.org/10.1109/TNSRE.2021.3059429.
• [98] 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 2019;7:28196–210. https://doi.org/10.1109/ ACCESS.2019.2901950.
• [99] Ke H, Chen D, Shah T, Liu X, Zhang X, Zhang L, et al. Cloudaided online EEG classification system for brain healthcare: a case study of depression evaluation with a lightweight CNN. Softw - Pract Exp 2020;50(5):596–610. https://doi.org/ 10.1002/spe.v50.510.1002/spe.2668.
• [100] Akbari H, Sadiq MT, Rehman AU. Classification of normal and depressed EEG signals based on centered correntropy of rhythms in empirical wavelet transform domain. Heal Inf Sci Syst 2021;9. doi: 10.1007/s13755-021-00139-7.
• [101] Loh HW, Ooi CP, Aydemir E, Tuncer T, Dogan S, Acharya UR. Decision support system for major depression detection using spectrogram and convolution neural network with EEG signals. Expert Syst 2021. https://doi.org/10.1111/ exsy.12773.
• [102] Wu C-T, Dillon D, Hsu H-C, Huang S, Barrick E, Liu Y-H. Depression detection using relative EEG power induced by emotionally positive images and a conformal kernel support vector machine. Appl Sci 2018;8(8):1244. https://doi.org/ 10.3390/app8081244.
• [103] Pei C, Sun Y, Zhu J, Wang X, Zhang Y, Zhang S, et al. Ensemble learning for early-response prediction of antidepressant treatment in major depressive disorder. J Magn Reson Imaging 2020;52(1):161–71. https://doi.org/ 10.1002/jmri.v52.110.1002/jmri.27029.
• [104] Nunez J-J, Nguyen TT, Zhou Y, Cao Bo, Ng RT, Chen J, et al. Replication of machine learning methods to predict treatment outcome with antidepressant medications in patients with major depressive disorder from STAR*D and CAN-BIND-1. PLoS ONE 2021;16(6):e0253023. https://doi.org/ 10.1371/journal.pone.0253023.
• [105] Perlman K, Benrimoh D, Israel S, Rollins C, Brown E, Tunteng J-F, et al. A systematic meta-review of predictors of antidepressant treatment outcome in major depressive disorder. J Affect Disord 2019;243:503–15. https://doi.org/ 10.1016/j.jad.2018.09.067.
• [106] Williams LM, Rush AJ, Koslow SH, Wisniewski SR, Cooper NJ, Nemeroff CB, et al. International Study to Predict Optimized Treatment for Depression (iSPOT-D), a randomized clinical trial: Rationale and protocol. Trials 2011;12(1). https://doi. Org/10.1186/1745-6215-12-4.
• [107] Lam RW, Milev R, Rotzinger S, Andreazza AC, Blier P, Brenner C, et al. Discovering biomarkers for antidepressant response: Protocol from the Canadian biomarker integration network in depression (CAN-BIND) and clinical characteristics of the first patient cohort. BMC Psychiatry 2016;16(1). https://doi.org/10.1186/s12888-016-0785-x.
• [108] Kennedy SH, Lam RL, Rotzinger S, et al. Symptomatic and functional outcomes and early prediction of response to escitalopram monotherapy and sequential adjunctive aripiprazole therapy in patients with major depressive disorder: a CAN-BIND-1 report. J Clin Psychiatry. 2019;80:18m12202.
• [109] Peng Z, Zhou C, Xue S, Bai J, Yu S, Li X, et al. Mechanism of repetitive transcranial magnetic stimulation for depression. Shanghai Arch Psychiatry 2018;30:84–92. https://doi.org/ 10.11919/j.issn.1002-0829.217047.
• [110] Tremblay S, Rogasch NC, Premoli I, Blumberger DM, Casarotto S, Chen R, et al. Clinical utility and prospective of TMS–EEG. Clin Neurophysiol 2019;130(5):802–44. https://doi. org/10.1016/j.clinph.2019.01.001.
• [111] Hasanzadeh F, Mohebbi M, Rostami R. Prediction of rTMS treatment response in major depressive disorder using machine learning techniques and nonlinear features of EEG signal. J Affect Disord 2019;256:132–42. https://doi.org/ 10.1016/j.jad.2019.05.070.
• [112] Čukić M. The reason why rTMS and tDCS are efficient in treatments of depression. Front Psychol 2020;10:1–5. https:// doi.org/10.3389/fpsyg.2019.02923.
• [113] Cukic M, Oommen J, Mutavdzic D, Jorgovanovic N, Ljubisavljevic M. The effect of single-pulse transcranial magnetic stimulation and peripheral nerve stimulation on complexity of EMG signal: fractal analysis. Exp Brain Res 2013;228(1):97–104. https://doi.org/10.1007/s00221-013-3541-1.
• [114] van der Vinne N, Vollebregt MA, Rush AJ, Eebes M, van Putten MJAM, Arns M. EEG biomarker informed prescription of antidepressants in MDD: a feasibility trial. Eur Neuropsychopharmacol 2021;44:14–22. https://doi.org/ 10.1016/j.euroneuro.2020.12.005.
• [115] van der Vinne N, Vollebregt MA, Boutros NN, Fallahpour K, van Putten MJAM, Arns M. Normalization of EEG in depression after antidepressant treatment with sertraline? A preliminary report. J Affect Disord 2019;259:67–72. https:// doi.org/10.1016/j.jad.2019.08.016.
• [116] Olejarczyk E, Zuchowicz U, Wozniak-Kwasniewska A, Kaminski M, Szekely D, David O. The impact of repetitive transcranial magnetic stimulation on functional connectivity in major depressive disorder and bipolar disorder evaluated by directed transfer function and indices based on graph theory. Int J Neural Syst 2020;30(04):2050015. https://doi.org/10.1142/S012906572050015X.
• [117] Olejarczyk E, Jozwik A, Valiulis V, Dapsys K, Gerulskis G, Germanavicius A. Statistical analysis of graph-theoretic indices to study EEG-TMS connectivity in patients with depression. Front Neuroinform 2021;15:1–9. https://doi.org/ 10.3389/fninf.2021.651082.
• [118] Olejarczyk E, Valiulis V, Dapsys K, Gerulskis G, Germanavicius A. Effect of repetitive transcranial magnetic stimulation on fronto-posterior and hemispheric asymmetry in depression. Biomed Signal Process Control 2021;68:102585. https://doi.org/10.1016/j.bspc.2021.102585.
• [119] Schiller MJ. Quantitative electroencephalography in guiding treatment of major depression. Front Psychiatry 2019;10:1–7. https://doi.org/10.3389/fpsyt.2018.00779.
• [120] Cao X, Deng C, Su X, Guo Y. Response and remission rates following high-frequency vs. Low-frequency repetitive transcranial magnetic stimulation (rTMS) over right DLPFC for treating major depressive disorder (MDD): a metaanalysis of randomized, double-blind trials. Front. Psychiatry 2018;9:1–7. https://doi.org/10.3389/ fpsyt.2018.00413.
• [121] Sadat Shahabi M, Shalbaf A, Maghsoudi A. Prediction of drug response in major depressive disorder using ensemble of transfer learning with convolutional neural network based on EEG. Biocybern Biomed Eng 2021;41(3):946–59. https://doi.org/10.1016/j.bbe.2021.06.006.
• [122] Zhdanov A, Atluri S, Wong W, Vaghei Y, Daskalakis ZJ, Blumberger DM, et al. Use of machine learning for predicting escitalopram treatment outcome from electroencephalography recordings in adult patients with depression. JAMA Netw Open 2020;3(1):e1918377. https:// doi.org/10.1001/jamanetworkopen.2019.18377.
• [123] Rajpurkar P, Yang J, Dass N, Vale V, Keller AS, Irvin J, et al. Evaluation of a machine learning model based on pretreatment symptoms and electroencephalographic features to predict outcomes of antidepressant treatment in adults with depression: a prespecified secondary analysis of a randomized clinical trial. JAMA Netw Open 2020;3(6): e206653. https://doi.org/10.1001/ jamanetworkopen.2020.6653.
• [124] Radenković, M. B. Č. (2021). Discussion on Y. Zhu, X. Wang, K. Mathiak, P. Toiviainen, T. Ristaniemi, J. Xu, Y. Chang and F. Cong, Altered EEG Oscillatory Brain Networks During Music-Listening in Major Depression, International Journal of Neural Systems, Vol. 31, No. 3 (2021) 2150001. Int J Neural Systems, 31(4), 2175001.
• [125] Kemp AH, Quintana DS, Quinn CR, Hopkinson P, Harris AWF. Major depressive disorder with melancholia displays robust alterations in resting state heart rate and its variability: Implications for future morbidity and mortality. Front Psychol 2014;5:1–9. .https://doi.org/10.3389/fpsyg.2014.01387.
• [126] Llamocca P, López V, Santos M, Čukić M. Personalized characterization of emotional states in patients with bipolar disorder. Mathematics 2021;9:1–18. https://doi.org/10.3390/ math9111174.
• [127] Whelan R, Garavan H. When optimism hurts: Inflated predictions in psychiatric neuroimaging. Biol Psychiatry 2014;75(9):746–8. https://doi.org/10.1016/j. Biopsych.2013.05.014.
• [128] Yahata N, Kasai K, Kawato M. Computational neuroscience approach to biomarkers and treatments for mental disorders. Psychiatry Clin Neurosci 2017;71(4):215–37. https://doi.org/10.1111/pcn.2017.71.issue-410.1111/pcn.12502.
• [129] Vázquez-Romero A, Gallardo-Antolín A. Automatic detection of depression in speech using ensemble convolutional neural networks. Entropy 2020;22(6):688. https://doi.org/10.3390/e22060688.
• [130] Espinola CW, Gomes JC, Pereira JMS, dos Santos WP. Detection of major depressive disorder using vocal acoustic analysis and machine learning—an exploratory study. Res Biomed Eng 2021;37(1):53–64. https://doi.org/10.1007/s42600-020-00100-9.
• [131] Tao X, Chi O, Delaney PJ, Li L, Huang J. Detecting depression using an ensemble classifier based on Quality of Life scales. Brain Informatics 2021;8(1). https://doi.org/10.1186/s40708- 021-00125-5.
• [132] Olejarczyk E, Bogucki P, Sobieszek A. The EEG split alpha peak: phenomenological origins and methodological aspects of detection and evaluation. Front Neurosci 2017;11:1–12. https://doi.org/10.3389/fnins.2017.00506.