Skip to main content
SpringerLink
Log in

Empowering precision medicine: AI-driven schizophrenia diagnosis via EEG signals: A comprehensive review from 2002–2023

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Schizophrenia (SZ) is a prevalent mental disorder characterized by cognitive, emotional, and behavioral changes. Symptoms of SZ include hallucinations, illusions, delusions, lack of motivation, and difficulties in concentration. While the exact causes of SZ remain unproven, factors such as brain injuries, stress, and psychotropic drugs have been implicated in its development. SZ can be classified into different types, including paranoid, disorganized, catatonic, undifferentiated, and residual. Diagnosing SZ involves employing various tools, including clinical interviews, physical examinations, psychological evaluations, the Diagnostic and Statistical Manual of Mental Disorders (DSM), and neuroimaging techniques. Electroencephalography (EEG) recording is a significant functional neuroimaging modality that provides valuable insights into brain function during SZ. However, EEG signal analysis poses challenges for neurologists and scientists due to the presence of artifacts, long-term recordings, and the utilization of multiple channels. To address these challenges, researchers have introduced artificial intelligence (AI) techniques, encompassing conventional machine learning (ML) and deep learning (DL) methods, to aid in SZ diagnosis. This study reviews papers focused on SZ diagnosis utilizing EEG signals and AI methods. The introduction section provides a comprehensive explanation of SZ diagnosis methods and intervention techniques. Subsequently, review papers in this field are discussed, followed by an introduction to the AI methods employed for SZ diagnosis and a summary of relevant papers presented in tabular form. Additionally, this study reports on the most significant challenges encountered in SZ diagnosis, as identified through a review of papers in this field. Future directions to overcome these challenges are also addressed. The discussion section examines the specific details of each paper, culminating in the presentation of conclusions and findings.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Data availability

This is a review paper and we do not use any data.

References

  1. Insel TR (2010) Rethinking schizophrenia. Nature 468(7321):187–193

    Google Scholar 

  2. McCutcheon RA, Marques TR, Howes OD (2020) Schizophrenia—an overview. JAMA Psychiat 77(2):201–210

    Google Scholar 

  3. Fletcher PC, Frith CD (2009) Perceiving is believing: a Bayesian approach to explaining the positive symptoms of schizophrenia. Nat Rev Neurosci 10(1):48–58

    Google Scholar 

  4. Andreasen NC (1982) Negative symptoms in schizophrenia: definition and reliability. Arch Gen Psychiatry 39(7):784–788

    Google Scholar 

  5. Simpson EH, Kellendonk C, Kandel E (2010) A possible role for the striatum in the pathogenesis of the cognitive symptoms of schizophrenia. Neuron 65(5):585–596

    Google Scholar 

  6. Henkel ND, Wu X, O’Donovan SM, Devine EA, Jiron JM, Rowland LM, McCullumsmith RE (2022) Schizophrenia: A disorder of broken brain bioenergetics. Mol Psychiatry 27(5):2393–2404

    Google Scholar 

  7. Batiuk MY, Tyler T, Dragicevic K, Mei S, Rydbirk R, Petukhov V, Khodosevich K (2022) Upper cortical layer–driven network impairment in schizophrenia. Science Advances 8(41):eabn8367

    Google Scholar 

  8. Dickerson FB, Lehman AF (2006) Evidence-based psychotherapy for schizophrenia. J Nerv Ment Dis 194(1):3–9

    Google Scholar 

  9. Smolak A, Gearing RE, Alonzo D, Baldwin S, Harmon S, McHugh K (2013) Social support and religion: mental health service use and treatment of schizophrenia. Community Ment Health J 49:444–450

    Google Scholar 

  10. Spaulding WD, Fleming SK, Reed D, Sullivan M, Storzbach D, Lam M (1999) Cognitive functioning in schizophrenia: implications for psychiatric rehabilitation. Schizophr Bull 25(2):275–289

    Google Scholar 

  11. Sun SX, Liu GG, Christensen DB, Fu AZ (2007) Review and analysis of hospitalization costs associated with antipsychotic nonadherence in the treatment of schizophrenia in the United States. Curr Med Res Opin 23(10):2305–2312

    Google Scholar 

  12. Zygmunt A, Olfson M, Boyer CA, Mechanic D (2002) Interventions to improve medication adherence in schizophrenia. Am J Psychiatry 159(10):1653–1664

    Google Scholar 

  13. Tharyan, P., & Adams, C. E. (2005). Electroconvulsive therapy for schizophrenia. Cochrane Database of Systematic Reviews, (2)

  14. Agarwal SM, Shivakumar V, Bose A, Subramaniam A, Nawani H, Chhabra H, Venkatasubramanian G (2013) Transcranial direct current stimulation in schizophrenia. Clinical Psychopharmacology and Neuroscience 11(3):118

    Google Scholar 

  15. Lindenmayer JP, Kulsa MKC, Sultana T, Kaur A, Yang R, Ljuri I, Khan A (2019) Transcranial direct-current stimulation in ultra-treatment-resistant schizophrenia. Brain Stimul 12(1):54–61

    Google Scholar 

  16. Corripio I, Roldán A, McKenna P, Sarró S, Alonso-Solis A, Salgado L, Portella M (2022) Target selection for deep brain stimulation in treatment resistant schizophrenia. Prog Neuropsychopharmacol Biol Psychiatry 112:110436

    Google Scholar 

  17. Turkington D, Dudley R, Warman DM, Beck AT (2006) Cognitive-behavioral therapy for schizophrenia: a review. Focus 10(2):5–233

    Google Scholar 

  18. Bermanzohn PC, Porto L, Arlow PB, Pollack S, Stronger R, Siris SG (2000) At issue: hierarchical diagnosis in chronic schizophrenia: a clinical study of co-occurring syndromes. Schizophr Bull 26(3):517–525

    Google Scholar 

  19. Martin CT (2016) The value of physical examination in mental health nursing. Nurse Educ Pract 17:91–96

    Google Scholar 

  20. Tsuang MT, Stone WS, Faraone SV (2000) Toward reformulating the diagnosis of schizophrenia. Am J Psychiatry 157(7):1041–1050

    Google Scholar 

  21. American Psychiatric Association (2013) Diagnostic and statistical manual of mental disorders: DSM-5 (vol 5, no 5). American Psychiatric Association, Washington, DC

  22. Kraguljac NV, McDonald WM, Widge AS, Rodriguez CI, Tohen M, Nemeroff CB (2021) Neuroimaging biomarkers in schizophrenia. Am J Psychiatry 178(6):509–521

    Google Scholar 

  23. Cooper R, Blashfield RK (2016) Re-evaluating DSM-I. Psychol Med 46(3):449–456

    Google Scholar 

  24. Horwitz AV (2014) DSM‐I and DSM‐II. The encyclopedia of clinical psychology, pp 1–6

  25. Spitzer RL, Williams JB, Skodol AE (1980) DSM-III: the major achievements and an overview. The American Journal of Psychiatry

  26. Krueger RF, Caspi A, Moffitt TE, Silva PA (1998) The structure and stability of common mental disorders (DSM-III-R): a longitudinal-epidemiological study. J Abnorm Psychol 107(2):216

    Google Scholar 

  27. Association AP, A. P., & American Psychiatric Association. (1994) Diagnostic and statistical manual of mental disorders: DSM-IV, vol 4. American psychiatric association, Washington, DC

    Google Scholar 

  28. Segal DL (2010) Diagnostic and statistical manual of mental disorders (DSM‐IV‐TR). The corsini encyclopedia of psychology, pp 1–3

  29. Sadeghi D, Shoeibi A, Ghassemi N, Moridian P, Khadem A, Alizadehsani R, Acharya UR (2022) An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works. Comput Biol Med 146:105554

  30. Shoeibi A, Ghassemi N, Khodatars M, Moridian P, Khosravi A, Zare A, Rajendra Acharya U (2022) Automatic diagnosis of schizophrenia and attention deficit hyperactivity disorder in rs-fMRI modality using convolutional autoencoder model and interval type-2 fuzzy regression. Cogn Neurodyn 17:1501–1523

    Google Scholar 

  31. Arbabshirani MR, Plis S, Sui J, Calhoun VD (2017) Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls. Neuroimage 145:137–165

    Google Scholar 

  32. Gur RE, Gur RC (2022) Functional magnetic resonance imaging in schizophrenia. Dialogues Clin Neurosci 12:333–343

    Google Scholar 

  33. Patel NH, Vyas NS, Puri BK, Nijran KS, Al-Nahhas A (2010) Positron emission tomography in schizophrenia: a new perspective. J Nucl Med 51(4):511–520

    Google Scholar 

  34. Laruelle M, Abi-Dargham A, Van Dyck CH, Gil R, D’Souza CD, Erdos J, Innis R (1996) Single photon emission computerized tomography imaging of amphetamine-induced dopamine release in drug-free schizophrenic subjects. Proc Natl Acad Sci 93(17):9235–9240

    Google Scholar 

  35. Shoeibi A, Rezaei M, Ghassemi N, Namadchian Z, Zare A, Gorriz JM (2022, May) Automatic diagnosis of schizophrenia in EEG signals using functional connectivity features and CNN-LSTM model. In: Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications: 9th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2022, Puerto de la Cruz, Tenerife, Spain, May 31–June 3, 2022, Proceedings, Part I. Springer International Publishing. Cham, pp 63–73

  36. Edgar JC, Guha A, Miller GA (2020) Magnetoencephalography for schizophrenia. Neuroimaging. Clinics 30(2):205–216

    Google Scholar 

  37. Dadgostar M, Setarehdan SK, Shahzadi S, Akin A (2018) Classification of schizophrenia using SVM via fNIRS. Biomedical Engineering: Applications, Basis and Communications 30(02):1850008

    Google Scholar 

  38. Kumar V, Shivakumar V, Chhabra H, Bose A, Venkatasubramanian G, Gangadhar BN (2017) Functional near infra-red spectroscopy (fNIRS) in schizophrenia: a review. Asian J Psychiatr 27:18–31

    Google Scholar 

  39. Srivastava NK, Khanra S, Chail V, Khess CR (2015) Clinical correlates of enlarged cavum septum pellucidum in schizophrenia: A revisit through computed tomography. Asian J Psychiatr 15:21–24

    Google Scholar 

  40. Samartzis L, Dima D, Fusar-Poli P, Kyriakopoulos M (2014) White matter alterations in early stages of schizophrenia: a systematic review of diffusion tensor imaging studies. J Neuroimaging 24(2):101–110

    Google Scholar 

  41. Rowland LM, Pradhan S, Korenic S, Wijtenburg SA, Hong LE, Edden RA, Barker PB (2016) Elevated brain lactate in schizophrenia: a 7 T magnetic resonance spectroscopy study. Transl Psychiatry 6(11):e967–e967

    Google Scholar 

  42. Shoeibi, A., Sadeghi, D., Moridian, P., Ghassemi, N., Heras, J., Alizadehsani, R., & Gorriz, J. M. (2021). Automatic diagnosis of schizophrenia in EEG signals using CNN-LSTM models. Frontiers in neuroinformatics, 58

  43. Shoeibi A, Moridian P, Khodatars M, Ghassemi N, Jafari M, Alizadehsani R, Acharya UR (2022) An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works. Comput Biol Med 149:106053

  44. Merlin Praveena D, Angelin Sarah D, Thomas George S (2022) Deep learning techniques for EEG signal applications–a review. IETE J Res 68(4):3030–3037

    Google Scholar 

  45. Shoeibi A, Ghassemi N, Khodatars M, Moridian P, Alizadehsani R, Zare A, Gorriz JM (2022) Detection of epileptic seizures on EEG signals using ANFIS classifier, autoencoders and fuzzy entropies. Biomed Signal Process Control 73:103417. https://doi.org/10.1016/j.bspc.2021.103417

  46. He, C., Chen, Y. Y., Phang, C. R., Stevenson, C., Chen, I. P., Jung, T. P., & Ko, L. W. (2023). Diversity and Suitability of the State-of-the-Art Wearable and Wireless EEG Systems Review. IEEE Journal of Biomedical and Health Informatics

  47. Cherian R, Kanaga EG (2022) Theoretical and methodological analysis of EEG based seizure detection and prediction: An exhaustive review. J Neurosci Methods 369:109483. https://doi.org/10.1016/j.jneumeth.2022.109483

  48. Acharya UR, Sree SV, Swapna G, Martis RJ, Suri JS (2013) Automated EEG analysis of epilepsy: a review. Knowl-Based Syst 45:147–165

    Google Scholar 

  49. Zhao Z, Wang C, Yuan Q, Zhao J, Ren Q, Xu Y, Yu Y (2020) Dynamic changes of brain networks during feedback-related processing of reinforcement learning in schizophrenia. Brain Res 1746:146979

    Google Scholar 

  50. Dvey-Aharon Z, Fogelson N, Peled A, Intrator N (2017) Connectivity maps based analysis of EEG for the advanced diagnosis of schizophrenia attributes. PLoS ONE 12(10):e0185852

    Google Scholar 

  51. Santos-Mayo L, San-José-Revuelta LM, Arribas JI (2016) A computer-aided diagnosis system with EEG based on the P3b wave during an auditory odd-ball task in schizophrenia. IEEE Trans Biomed Eng 64(2):395–407

    Google Scholar 

  52. Goshvarpour A, Goshvarpour A (2020) Schizophrenia diagnosis using innovative EEG feature-level fusion schemes. Physical and Engineering Sciences in Medicine 43(1):227–238

    Google Scholar 

  53. Sahu PK (2023) Artificial intelligence system for verification of schizophrenia via theta-EEG rhythm. Biomed Signal Process Control 81:104485. https://doi.org/10.1016/j.bspc.2022.104485

  54. de Miras JR, Ibáñez-Molina AJ, Soriano MF, Iglesias-Parro S (2023) Schizophrenia classification using machine learning on resting state EEG signal. Biomed Signal Process Control 79:104233

    Google Scholar 

  55. Li F, Jiang L, Liao Y, Li C, Zhang Q, Zhang S, Dai J (2022) Recognition of the Multi-class Schizophrenia Based on the Resting-State EEG Network Topology. Brain Topogr 35(4):495–506

    Google Scholar 

  56. de Filippis, R., Carbone, E. A., Gaetano, R., Bruni, A., Pugliese, V., Segura-Garcia, C., & De Fazio, P. (2019). Machine learning techniques in a structural and functional MRI diagnostic approach in schizophrenia: a systematic review. Neuropsychiatric disease and treatment, 1605–1627

  57. Steardo L Jr, Carbone EA, De Filippis R, Pisanu C, Segura-Garcia C, Squassina A, Steardo L (2020) Application of support vector machine on fMRI data as biomarkers in schizophrenia diagnosis: a systematic review. Front Psych 11:588

    Google Scholar 

  58. Lai JW, Ang CKE, Acharya UR, Cheong KH (2021) Schizophrenia: a survey of artificial intelligence techniques applied to detection and classification. Int J Environ Res Public Health 18(11):6099

    Google Scholar 

  59. Cortes-Briones JA, Tapia-Rivas NI, D’Souza DC, Estevez PA (2022) Going deep into schizophrenia with artificial intelligence. Schizophr Res 245:122–140

    Google Scholar 

  60. Verma, S., Goel, T., Tanveer, M., Ding, W., Sharma, R., & Murugan, R. (2023). Machine learning techniques for the Schizophrenia diagnosis: A comprehensive review and future research directions. arXiv preprint arXiv:2301.07496

  61. Barros C, Silva CA, Pinheiro AP (2021) Advanced EEG-based learning approaches to predict schizophrenia: Promises and pitfalls. Artif Intell Med 114:102039

    Google Scholar 

  62. Luján MÁ, Jimeno MV, Mateo Sotos J, Ricarte JJ, Borja AL (2021) A survey on eeg signal processing techniques and machine learning: Applications to the neurofeedback of autobiographical memory deficits in schizophrenia. Electronics 10(23):3037. https://doi.org/10.3390/electronics10233037

  63. Moher D, Liberati A, Tetzlaff J, Altman DG, T PRISMA Group* (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann Intern Med 151(4):264–269

    Google Scholar 

  64. Khan P, Kader MF, Islam SR, Rahman AB, Kamal MS, Toha MU, Kwak KS (2021) Machine learning and deep learning approaches for brain disease diagnosis: principles and recent advances. IEEE Access 9:37622–37655

    Google Scholar 

  65. Cho G, Yim J, Choi Y, Ko J, Lee SH (2019) Review of machine learning algorithms for diagnosing mental illness. Psychiatry Investig 16(4):262

    Google Scholar 

  66. Zhang L, Wang M, Liu M, Zhang D (2020) A survey on deep learning for neuroimaging-based brain disorder analysis. Front Neurosci 14:779

    Google Scholar 

  67. Shatte AB, Hutchinson DM, Teague SJ (2019) Machine learning in mental health: a scoping review of methods and applications. Psychol Med 49(9):1426–1448

    Google Scholar 

  68. Tzimourta KD, Christou V, Tzallas AT, Giannakeas N, Astrakas LG, Angelidis P, Tsipouras MG (2021) Machine learning algorithms and statistical approaches for Alzheimer’s disease analysis based on resting-state EEG recordings: A systematic review. Int J Neural Syst 31(05):2130002

    Google Scholar 

  69. Rasheed K, Qayyum A, Qadir J, Sivathamboo S, Kwan P, Kuhlmann L, Razi A (2020) Machine learning for predicting epileptic seizures using EEG signals: A review. IEEE Rev Biomed Eng 14:139–155. https://doi.org/10.1109/rbme.2020.3008792

    Article  Google Scholar 

  70. Shoeibi A, Khodatars M, Jafari M, Ghassemi N, Moridian P, Alizadesani R, Gorriz JM (2022) Diagnosis of brain diseases in fusion of neuroimaging modalities using deep learning: a review. Inf Fusion 93:85–117. https://doi.org/10.1016/j.inffus.2022.12.010

    Article  Google Scholar 

  71. Pathak, D., Kashyap, R., & Rahamatkar, S. (2022). A study of deep learning approach for the classification of electroencephalogram (EEG) brain signals. In Artificial Intelligence and Machine Learning for EDGE Computing (pp. 133–144). Academic Press

  72. Shoeibi A, Ghassemi N, Alizadehsani R, Rouhani M, Hosseini-Nejad H, Khosravi A, Nahavandi S (2021) A comprehensive comparison of handcrafted features and convolutional autoencoders for epileptic seizures detection in EEG signals. Expert Syst Appl 163:113788. https://doi.org/10.1016/j.eswa.2020.113788

  73. Oh SL, Hagiwara Y, Raghavendra U, Yuvaraj R, Arunkumar N, Murugappan M, Acharya UR (2020) A deep learning approach for Parkinson’s disease diagnosis from EEG signals. Neural Comput Appl 32:10927–10933

    Google Scholar 

  74. Yasin S, Hussain SA, Aslan S, Raza I, Muzammel M, Othmani A (2021) EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks: A review. Comput Methods Programs Biomed 202:106007

    Google Scholar 

  75. Olejarczyk E, Jernajczyk W (2017) Graph-based analysis of brain connectivity in schizophrenia. PLoS ONE 12(11):e0188629

    Google Scholar 

  76. https://www.kaggle.com/datasets/broach/button-tone-sz

  77. Kim, S. P. (2018). Preprocessing of EEG. Computational EEG Analysis: Methods and Applications, 15–33

  78. Sazgar, M., Young, M. G., Sazgar, M., & Young, M. G. (2019). EEG artifacts. Absolute Epilepsy and EEG Rotation Review: Essentials for Trainees, 149–162

  79. Chen X, Xu X, Liu A, Lee S, Chen X, Zhang X, Wang ZJ (2019) Removal of muscle artifacts from the EEG: A review and recommendations. IEEE Sens J 19(14):5353–5368

    Google Scholar 

  80. Crespo-Garcia M, Atienza M, Cantero JL (2008) Muscle artifact removal from human sleep EEG by using independent component analysis. Ann Biomed Eng 36:467–475

    Google Scholar 

  81. Chen X, Liu A, Peng H, Ward RK (2014) A preliminary study of muscular artifact cancellation in single-channel EEG. Sensors 14(10):18370–18389

    Google Scholar 

  82. Lakshmi MR, Prasad TV, Prakash DVC (2014) Survey on EEG signal processing methods. Int J Adv Res Comput Scie Softw Eng 4(1)

  83. Thakor NV, Sherman DL (2012) EEG signal processing: Theory and applications. Neural Engineering. Springer, US, Boston, MA, pp 259–303

    Google Scholar 

  84. Laton J, Van Schependom J, Gielen J, Decoster J, Moons T, De Keyser J, Nagels G (2014) Single-subject classification of schizophrenia patients based on a combination of oddball and mismatch evoked potential paradigms. J Neurol Sci 347(1–2):262–267

    Google Scholar 

  85. Prabhu, S., & Martis, R. J. (2020, July). Diagnosis of schizophrenia using Kolmogorov complexity and sample entropy. In 2020 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT) (pp. 1–4). IEEE

  86. Babiloni C, Pennica A, Del Percio C, Noce G, Cordone S, Lopez S, Andreoni M (2016) Antiretroviral therapy affects the z-score index of deviant cortical EEG rhythms in naïve HIV individuals. NeuroImage Clin 12:144–156. https://doi.org/10.1016/j.nicl.2016.06.005

    Article  Google Scholar 

  87. Kwak, Y., Kong, K., Song, W. J., & Kim, S. E. (2023). Subject-Invariant Deep Neural Networks based on Baseline Correction for EEG Motor Imagery BCI. IEEE Journal of Biomedical and Health Informatics

  88. Delorme A, Makeig S (2004) EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods 134(1):9–21

    Google Scholar 

  89. Prabhakar, S. K., Rajaguru, H., & Kim, S. H. (2020). Schizophrenia EEG signal classification based on swarm intelligence computing. Computational Intelligence and Neuroscience, 2020

  90. Chu WL, Huang MW, Jian BL, Cheng KS (2017) Analysis of EEG entropy during visual evocation of emotion in schizophrenia. Ann Gen Psychiatry 16(1):1–9

    Google Scholar 

  91. Siuly S, Khare SK, Bajaj V, Wang H, Zhang Y (2020) A computerized method for automatic detection of schizophrenia using EEG signals. IEEE Trans Neural Syst Rehabil Eng 28(11):2390–2400. https://doi.org/10.1109/tnsre.2020.3022715

    Article  Google Scholar 

  92. Masychev K, Ciprian C, Ravan M (2020, December) Machine learning approach to diagnose schizophrenia based on effective connectivity of resting EEG data. In: 2020 IEEE Signal Processing in Medicine and Biology Symposium (SPMB). IEEE, pp 1–6

  93. Khare SK, Bajaj V (2021) A self-learned decomposition and classification model for schizophrenia diagnosis. Comput Methods Programs Biomed 211:106450

    Google Scholar 

  94. Aslan, Z., & Akin, M. (2020). Automatic Detection of Schizophrenia by Applying Deep Learning over Spectrogram Images of EEG Signals. Traitement du Signal, 37(2)

  95. Saeedi, M., Saeedi, A., & Mohammadi, P. (2022). Schizophrenia Diagnosis via FFT and Wavelet Convolutional Neural Networks utilizing EEG signals

  96. Akbari H, Ghofrani S, Zakalvand P, Sadiq MT (2021) Schizophrenia recognition based on the phase space dynamic of EEG signals and graphical features. Biomed Signal Process Control 69:102917. https://doi.org/10.1016/j.bspc.2021.102917

  97. Shalbaf A, Bagherzadeh S, Maghsoudi A (2020) Transfer learning with deep convolutional neural network for automated detection of schizophrenia from EEG signals. Physical and Engineering Sciences in Medicine 43:1229–1239

    Google Scholar 

  98. Phang CR, Ting CM, Samdin SB, Ombao H (2019, March) Classification of EEG-based effective brain connectivity in schizophrenia using deep neural networks. In: 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER). IEEE, pp 401–406

  99. Alves CL, Pineda AM, Roster K, Thielemann C, Rodrigues FA (2022) EEG functional connectivity and deep learning for automatic diagnosis of brain disorders: Alzheimer’s disease and schizophrenia. Journal of Physics: Complexity 3(2):025001. https://doi.org/10.1088/2632-072x/ac5f8d

  100. AlSharabi K, Salamah YB, Abdurraqeeb AM, Aljalal M, Alturki FA (2022) EEG signal processing for Alzheimer’s disorders using discrete wavelet transform and machine learning approaches. IEEE Access 10:89781–89797

    Google Scholar 

  101. Xu S, Wang Z, Sun J, Zhang Z, Wu Z, Yang T, Cheng C (2020) Using a deep recurrent neural network with EEG signal to detect Parkinson’s disease. Ann Transl Med 8(14):874–874. https://doi.org/10.21037/atm-20-5100

  102. Shoeibi A, Khodatars M, Ghassemi N, Jafari M, Moridian P, Alizadehsani R, Acharya UR (2021) Epileptic seizures detection using deep learning techniques: A review. Int J Environ Res Public Health 18(11):5780. https://doi.org/10.3390/ijerph18115780

  103. Mutlag WK, Ali SK, Aydam ZM, Taher BH (2020) Feature extraction methods: a review. J Phys Conf Ser 1591(1):012028. IOP Publishing

  104. Galar M, Derrac J, Peralta D, Triguero I, Paternain D, Lopez-Molina C, Herrera F (2015) A survey of fingerprint classification Part I: Taxonomies on feature extraction methods and learning models. Knowl-Based Syst 81:76–97

    Google Scholar 

  105. Diykh M, Li Y, Wen P (2016) EEG sleep stages classification based on time domain features and structural graph similarity. IEEE Trans Neural Syst Rehabil Eng 24(11):1159–1168

    Google Scholar 

  106. Ramos-Aguilar R, Olvera-López JA, Olmos-Pineda I, Sánchez-Urrieta S (2020) Feature extraction from EEG spectrograms for epileptic seizure detection. Pattern Recogn Lett 133:202–209

    Google Scholar 

  107. Al-Fahoum, A. S., & Al-Fraihat, A. A. (2014). Methods of EEG signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices, 2014

  108. Li M, Chen W, Zhang T (2017) Automatic epileptic EEG detection using DT-CWT-based non-linear features. Biomed Signal Process Control 34:114–125

    Google Scholar 

  109. Boostani R, Sadatnezhad K, Sabeti M (2009) An efficient classifier to diagnose of schizophrenia based on the EEG signals. Expert Syst Appl 36(3):6492–6499

    Google Scholar 

  110. Azizi, S., Hier, D. B., & Wunsch, D. C. (2021, November). Schizophrenia classification using resting state EEG functional connectivity: source level outperforms sensor level. In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 1770–1773). IEEE

  111. Khare, S. K., Bajaj, V., Siuly, S., & Sinha, G. R. (2020). Classification of schizophrenia patients through empirical wavelet transformation using electroencephalogram signals. In Modelling and Analysis of Active Biopotential Signals in Healthcare, Volume 1. IOP Publishing

  112. Ellis, C. A., Sattiraju, A., Miller, R., & Calhoun, V. (2022, November). Examining Reproducibility of EEG Schizophrenia Biomarkers Across Explainable Machine Learning Models. In 2022 IEEE 22nd International Conference on Bioinformatics and Bioengineering (BIBE) (pp. 305–308). IEEE

  113. Thilakavathi B, Shenbaga Devi S, Malaiappan M, Bhanu K (2019) EEG power spectrum analysis for schizophrenia during mental activity. Australas Phys Eng Sci Med 42(3):887–897

    Google Scholar 

  114. Knott V, Mahoney C, Kennedy S, Evans K (2001) EEG power, frequency, asymmetry and coherence in male depression. Psychiatry Research: Neuroimaging 106(2):123–140

    Google Scholar 

  115. Mormann F, Lehnertz K, David P, Elger CE (2000) Mean phase coherence as a measure for phase synchronization and its application to the EEG of epilepsy patients. Physica D 144(3–4):358–369

    Google Scholar 

  116. WeiKoh JE, Rajinikanth V, Vicnesh J, Pham TH, Oh SL, Yeong CH, Cheong KH (2022) Application of local configuration pattern for automated detection of schizophrenia with electroencephalogram signals. Expert Syst:e12957

  117. Johannesen JK, Bi J, Jiang R, Kenney JG, Chen CMA (2016) Machine learning identification of EEG features predicting working memory performance in schizophrenia and healthy adults. Neuropsychiatric electrophysiology 2(1):1–21

    Google Scholar 

  118. Baygin M, Dogan S, Tuncer T, Barua PD, Faust O, Arunkumar N, Acharya UR (2021) Automated ASD detection using hybrid deep lightweight features extracted from EEG signals. Computers in Biology and Medicine 134:104548

    Google Scholar 

  119. Acharya UR, Hagiwara Y, Deshpande SN, Suren S, Koh JEW, Oh SL, Lim CM (2019) Characterization of focal EEG signals: a review. Futur Gener Comput Syst 91:290–299. https://doi.org/10.1016/j.future.2018.08.044

    Article  Google Scholar 

  120. Boonyakitanont P, Lek-Uthai A, Chomtho K, Songsiri J (2020) A review of feature extraction and performance evaluation in epileptic seizure detection using EEG. Biomed Signal Process Control 57:101702

    Google Scholar 

  121. Zang B, Lin Y, Liu Z, Gao X (2021) A deep learning method for single-trial EEG classification in RSVP task based on spatiotemporal features of ERPs. Journal of Neural Engineering 18(4):0460c8

    Google Scholar 

  122. Abibullaev B, Zollanvari A (2019) Learning discriminative spatiospectral features of ERPs for accurate brain–computer interfaces. IEEE J Biomed Health Inform 23(5):2009–2020

    Google Scholar 

  123. Lemm S, Curio G, Hlushchuk Y, Muller KR (2006) Enhancing the signal-to-noise ratio of ICA-based extracted ERPs. IEEE Trans Biomed Eng 53(4):601–607

    Google Scholar 

  124. Sobahi N, Ari B, Cakar H, Alcin OF, Sengur A (2022) A new signal to image mapping procedure and convolutional neural networks for efficient schizophrenia detection in eeg recordings. IEEE Sens J 22(8):7913–7919

    Google Scholar 

  125. Dvey-Aharon Z, Fogelson N, Peled A, Intrator N (2015) Schizophrenia detection and classification by advanced analysis of EEG recordings using a single electrode approach. PLoS ONE 10(4):e0123033. https://doi.org/10.1371/journal.pone.0123033

  126. Baygin M (2021) An accurate automated schizophrenia detection using TQWT and statistical moment based feature extraction. Biomed Signal Process Control 68:102777

    Google Scholar 

  127. Ayesha S, Hanif MK, Talib R (2020) Overview and comparative study of dimensionality reduction techniques for high dimensional data. Information Fusion 59:44–58

    Google Scholar 

  128. Reddy GT, Reddy MPK, Lakshmanna K, Kaluri R, Rajput DS, Srivastava G, Baker T (2020) Analysis of dimensionality reduction techniques on big data. Ieee Access 8:54776–54788. https://doi.org/10.1109/ACCESS.2020.2980942

    Article  Google Scholar 

  129. Bougou, V., Mporas, I., Schirmer, P., & Ganchev, T. (2019, November). Evaluation of EEG Connectivity Network Measures based Features in Schizophrenia Classification. In 2019 International Conference on Biomedical Innovations and Applications (BIA) (pp. 1–4). IEEE

  130. Espadoto M, Martins RM, Kerren A, Hirata NS, Telea AC (2019) Toward a quantitative survey of dimension reduction techniques. IEEE Trans Visual Comput Graphics 27(3):2153–2173

    Google Scholar 

  131. Palo, H. K., Sahoo, S., & Subudhi, A. K. (2021). Dimensionality reduction techniques: Principles, benefits, and limitations. Data Analytics in Bioinformatics: A Machine Learning Perspective, 77–107

  132. Hussain A, Kim CH, Mehdi A (2021) A comprehensive review of intelligent islanding schemes and feature selection techniques for distributed generation system. IEEE Access 9:146603–146624

    Google Scholar 

  133. Kumar, R. A., Franklin, J. V., & Koppula, N. (2022). A comprehensive survey on metaheuristic algorithm for feature selection techniques. Materials Today: Proceedings

  134. Ke PF, Xiong DS, Li JH, Pan ZL, Zhou J, Li SJ, Wu K (2021) An integrated machine learning framework for a discriminative analysis of schizophrenia using multi-biological data. Sci Rep 11(1):1–11

    Google Scholar 

  135. Li F, Wang J, Liao Y, Yi C, Jiang Y, Si Y, Xu P (2019) Differentiation of schizophrenia by combining the spatial EEG brain network patterns of rest and task P300. IEEE Trans Neural Syst Rehabil Eng 27(4):594–602

    Google Scholar 

  136. Anowar F, Sadaoui S, Selim B (2021) Conceptual and empirical comparison of dimensionality reduction algorithms (pca, kpca, lda, mds, svd, lle, isomap, le, ica, t-sne). Computer Science Review 40:100378

    MathSciNet  Google Scholar 

  137. Wang Y, Cang S, Yu H (2019) Mutual information inspired feature selection using kernel canonical correlation analysis. Expert Syst Appl X 4:100014. https://doi.org/10.1016/j.eswax.2019.100014

  138. Song XF, Zhang Y, Gong DW, Sun XY (2021) Feature selection using bare-bones particle swarm optimization with mutual information. Pattern Recogn 112:107804. https://doi.org/10.1016/j.patcog.2020.107804

  139. Maldonado, J., Riff, M. C., & Neveu, B. (2022). A review of recent approaches on wrapper feature selection for intrusion detection. Expert Systems with Applications, 116822

  140. Nafis NSM, Awang S (2021) An enhanced hybrid feature selection technique using term frequency-inverse document frequency and support vector machine-recursive feature elimination for sentiment classification. IEEE Access 9:52177–52192

    Google Scholar 

  141. Zhang H, Wang J, Sun Z, Zurada JM, Pal NR (2019) Feature selection for neural networks using group lasso regularization. IEEE Trans Knowl Data Eng 32(4):659–673

    Google Scholar 

  142. Thilakvathi B, Devi SS, Bhanu K, Malaippan M (2017) EEG signal complexity analysis for schizophrenia during rest and mental activity. Biomedical Research-India 28(1):1–9

    Google Scholar 

  143. Buettner, R., Hirschmiller, M., Schlosser, K., Rössle, M., Fernandes, M., & Timm, I. J. (2019, October). High-performance exclusion of schizophrenia using a novel machine learning method on EEG data. In 2019 IEEE International Conference on E-Health Networking, Application & Services (HealthCom) (pp. 1–6). IEEE

  144. Vasios C, Papageorgiou C, Matsopoulos GK, Nikita KS, Uzunoglu N (2002) A decision support system of evoked potentials for the classification of patients with first-episode schizophrenia. German Journal of Psychiatry 5:78–84

    Google Scholar 

  145. Chang Q, Li C, Zhang J, Wang C (2022) Dynamic brain functional network based on EEG microstate during sensory gating in schizophrenia. J Neural Eng 19(2):026007

    Google Scholar 

  146. Najafzadeh H, Esmaeili M, Farhang S, Sarbaz Y, Rasta SH (2021) Automatic classification of schizophrenia patients using resting-state EEG signals. Physical and Engineering Sciences in Medicine 44(3):855–870

    Google Scholar 

  147. Prabhakar SK, Rajaguru H, Lee SW (2020) A framework for schizophrenia EEG signal classification with nature inspired optimization algorithms. IEEE Access 8:39875–39897

    Google Scholar 

  148. Rajesh, K. N., & Kumar, T. S. (2021, November). Schizophrenia Detection in Adolescents from EEG Signals using Symmetrically weighted Local Binary Patterns. In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 963–966). IEEE

  149. Sharma M, Acharya UR (2021) Automated detection of schizophrenia using optimal wavelet-based l 1 norm features extracted from single-channel EEG. Cogn Neurodyn 15(4):661–674

    Google Scholar 

  150. Xin J, Zhou K, Wang Z, Wang Z, Chen J, Wang X, Chen Q (2022) Hybrid High-order Brain Functional Networks for Schizophrenia-Aided Diagnosis. Cogn Comput 14(4):1303–1315

    Google Scholar 

  151. Almutairi MM, Alhamad N, Alyami A, Alshobbar Z, Alfayez H, Al-Akkas N, Olatunji SO (2019) Preemptive diagnosis of schizophrenia disease using computational intelligence techniques. In: 2019 2nd International Conference on Computer Applications & Information Security (ICCAIS). IEEE, pp 1–6

  152. Zhao Q, Hu B, Li Y, Peng H, Li L, Liu Q, Feng J (2013) An Alpha resting EEG study on nonlinear dynamic analysis for schizophrenia. In: 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER). IEEE, pp 484–488

  153. Hiesh, M. H., Andy, Y. Y. L., Shen, C. P., Chen, W., Lin, F. S., Sung, H. Y., & Lai, F. (2013, July). Classification of schizophrenia using genetic algorithm-support vector machine (ga-svm). In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 6047–6050). IEEE

  154. Zhang, L. (2019, July). EEG signals classification using machine learning for the identification and diagnosis of schizophrenia. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 4521–4524). IEEE

  155. Kim JY, Lee HS, Lee SH (2020) EEG source network for the diagnosis of schizophrenia and the identification of subtypes based on symptom severity—A machine learning approach. J Clin Med 9(12):3934

    Google Scholar 

  156. Shim M, Hwang HJ, Kim DW, Lee SH, Im CH (2016) Machine-learning-based diagnosis of schizophrenia using combined sensor-level and source-level EEG features. Schizophr Res 176(2–3):314–319

    Google Scholar 

  157. Sabeti M, Behroozi R, Moradi E (2016) Analysing complexity, variability and spectral measures of schizophrenic EEG signal. Int J Biomed Eng Technol 21(2):109–127

    Google Scholar 

  158. Luján, M. Á., Sotos, J. M., Santos, J. L., & Borja, A. L. (2022). Accurate Neural Network Classification Model for Schizophrenia Disease Based on Electroencephalogram Data

  159. Febles, E. S., Ortega, M. O., Sosa, M. V., & Sahli, H. (2022). Machine Learning techniques for the diagnosis of Schizophrenia based on Event Related Potentials. medRxiv

  160. Khare SK, Bajaj V (2022) A hybrid decision support system for automatic detection of Schizophrenia using EEG signals. Comput Biol Med 141:105028

    Google Scholar 

  161. Goshvarpour A, Goshvarpour A (2022) Schizophrenia diagnosis by weighting the entropy measures of the selected EEG channel. Journal of Medical and Biological Engineering 42(6):898–908

    Google Scholar 

  162. Sairamya NJ, Subathra MSP, George ST (2022) Automatic identification of schizophrenia using EEG signals based on discrete wavelet transform and RLNDiP technique with ANN. Expert Syst Appl 192:116230. https://doi.org/10.1016/j.eswa.2021.116230

  163. Kumar TS, Rajesh KN, Maheswari S, Kanhangad V, Acharya UR (2023) Automated Schizophrenia detection using local descriptors with EEG signals. Eng Appl Artif Intell 117:105602. https://doi.org/10.1016/j.engappai.2022.105602

  164. Balasubramanian, K., Ramya, K., & Gayathri Devi, K. (2022). Optimized adaptive neuro-fuzzy inference system based on hybrid grey wolf-bat algorithm for schizophrenia recognition from EEG signals. Cognitive Neurodynamics, 1–19

  165. Baradits M, Bitter I, Czobor P (2020) Multivariate patterns of EEG microstate parameters and their role in the discrimination of patients with schizophrenia from healthy controls. Psychiatry Res 288:112938

    Google Scholar 

  166. Keihani A, Sajadi SS, Hasani M, Ferrarelli F (2022) Bayesian optimization of machine learning classification of resting-state EEG microstates in schizophrenia: a proof-of-concept preliminary study based on secondary analysis. Brain Sci 12(11):1497

    Google Scholar 

  167. Aydemir E, Dogan S, Baygin M, Ooi CP, Barua PD, Tuncer T, Acharya UR (2022) CGP17Pat: Automated schizophrenia detection based on a cyclic group of prime order patterns using EEG signals. Healthcare 10(4):643. MDPI

  168. Jahmunah V, Oh SL, Rajinikanth V, Ciaccio EJ, Cheong KH, Arunkumar N, Acharya UR (2019) Automated detection of schizophrenia using nonlinear signal processing methods. Artif Intell Med 100:1016980. https://doi.org/10.1016/j.artmed.2019.07.006

  169. Min B, Kim M, Lee J, Byun JI, Chu K, Jung KY, Kwon JS (2020) Prediction of individual responses to electroconvulsive therapy in patients with schizophrenia: Machine learning analysis of resting-state electroencephalography. Schizophr Res 216:147–153

    Google Scholar 

  170. Kim K, Duc NT, Choi M, Lee B (2021) EEG microstate features for schizophrenia classification. PLoS ONE 16(5):e0251842

    Google Scholar 

  171. Baygin M, Yaman O, Tuncer T, Dogan S, Barua PD, Acharya UR (2021) Automated accurate schizophrenia detection system using Collatz pattern technique with EEG signals. Biomed Signal Process Control 70:102936

    Google Scholar 

  172. Ciprian C, Masychev K, Ravan M, Manimaran A, Deshmukh A (2021) Diagnosing schizophrenia using effective connectivity of resting-state EEG data. Algorithms 14(5):139

    Google Scholar 

  173. Das K, Pachori RB (2021) Schizophrenia detection technique using multivariate iterative filtering and multichannel EEG signals. Biomed Signal Process Control 67:102525

    Google Scholar 

  174. Aksöz, A., Akyüz, D., BAYIR, F., YILDIZ, N. C., Orhanbulucu, F., & Latifoğlu, F. Analysis and Classification of Schizophrenia Using Event Related Potential Signals. Computer Science, 32–36

  175. URAL, A. B., & Uğur, E. R. A. Y. AUTOMATED PSYCHIATRIC DATA ANALYSIS from SINGLE CHANNEL EEG with SIGNAL PROCESSING and ARTIFICIAL INTELLIGENCE METHODS. Journal of Scientific Reports-A, (050), 106–123

  176. Krishnan PT, Raj ANJ, Balasubramanian P, Chen Y (2020) Schizophrenia detection using MultivariateEmpirical Mode Decomposition and entropy measures from multichannel EEG signal. Biocybernetics and Biomedical Engineering 40(3):1124–1139. https://doi.org/10.1016/j.bbe.2020.05.008

    Article  Google Scholar 

  177. Góngora Alonso S, Marques G, Agarwal D, De la Torre Díez I, Franco-Martín M (2022) Comparison of machine learning algorithms in the prediction of hospitalized patients with schizophrenia. Sensors 22(7):2517

    Google Scholar 

  178. Liu H, Zhang T, Ye Y, Pan C, Yang G, Wang J, Qiu RC (2017) A data driven approach for resting-state EEG signal classification of schizophrenia with control participants using random matrix theory. arXiv preprint arXiv:1712.05289

  179. Devia C, Mayol-Troncoso R, Parrini J, Orellana G, Ruiz A, Maldonado PE, Egaña JI (2019) EEG classification during scene free-viewing for schizophrenia detection. IEEE Trans Neural Syst Rehabil Eng 27(6):1193–1199

    Google Scholar 

  180. Luo Y, Tian Q, Wang C, Zhang K, Wang C, Zhang J (2020) Biomarkers for prediction of schizophrenia: Insights from resting-state EEG microstates. IEEE Access 8:213078–213093

    Google Scholar 

  181. Agarwal M, Singhal A (2023) Fusion of pattern-based and statistical features for Schizophrenia detection from EEG signals. Med Eng Phys 112:103949. https://doi.org/10.1016/j.medengphy.2023.103949

  182. Baygin, M., Barua, P. D., Chakraborty, S., Tuncer, I., Dogan, S., Palmer, E. E., & Acharya, U. R. (2023). CCPNet136: automated detection of schizophrenia using carbon chain pattern and iterative TQWT technique with EEG signals. Physiological Measurement

  183. Gosala B, Kapgate PD, Jain P, Chaurasia RN, Gupta M (2023) Wavelet transforms for feature engineering in EEG data processing: An application on Schizophrenia. Biomed Signal Process Control 85:104811

    Google Scholar 

  184. Shanarova N, Pronina M, Lipkovich M, Ponomarev V, Müller A, Kropotov J (2023) Application of Machine Learning to Diagnostics of Schizophrenia Patients Based on Event-Related Potentials. Diagnostics 13(3):509

    Google Scholar 

  185. Aversano L, Bernardi ML, Cimitile M, Pecori R (2021) A systematic review on Deep Learning approaches for IoT security. Comput Sci Rev 40:100389. https://doi.org/10.1016/j.cosrev.2021.100389

  186. Zeng C, Gu L, Liu Z, Zhao S (2020) Review of deep learning approaches for the segmentation of multiple sclerosis lesions on brain MRI. Front Neuroinform 14:610967. https://doi.org/10.3389/fninf.2020.610967

  187. Jafari M, Shoeibi A, Khodatars M, Ghassemi N, Moridian P, Alizadehsani R, Acharya UR (2023) Automated diagnosis of cardiovascular diseases from cardiac magnetic resonance imaging using deep learning models: a review. Comput Biol Med 160:106998. https://doi.org/10.1016/j.compbiomed.2023.106998

  188. Moridian P, Shoeibi A, Khodatars M, Jafari M, Pachori RB, Khadem A, Ling SH (2022) Automatic diagnosis of sleep apnea from biomedical signals using artificial intelligence techniques: Methods, challenges, and future works. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 12(6):e1478

    Google Scholar 

  189. Moridian, P., Ghassemi, N., Jafari, M., Salloum-Asfar, S., Sadeghi, D., Khodatars, M., ... & Acharya, U. R. (2022). Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review. arXiv preprint arXiv:2206.11233

  190. Huang SC, Pareek A, Seyyedi S, Banerjee I, Lungren MP (2020) Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines. NPJ digital medicine 3(1):136

    Google Scholar 

  191. Zhou T, Ruan S, Canu S (2019) A review: deep learning for medical image segmentation using multi-modality fusion. Array 3:100004. https://doi.org/10.1016/j.array.2019.100004

  192. Aggarwal R, Sounderajah V, Martin G, Ting DS, Karthikesalingam A, King D, Darzi A (2021) Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis. NPJ digital medicine 4(1):65

    Google Scholar 

  193. Chen, X., Wang, X., Zhang, K., Fung, K. M., Thai, T. C., Moore, K., Qiu, Y. (2022). Recent advances and clinical applications of deep learning in medical image analysis. Medical Image Analysis, 102444

  194. Van der Laak J, Litjens G, Ciompi F (2021) Deep learning in histopathology: the path to the clinic. Nat Med 27(5):775–784

    Google Scholar 

  195. Budd S, Robinson EC, Kainz B (2021) A survey on active learning and human-in-the-loop deep learning for medical image analysis. Med Image Anal 71:102062

    Google Scholar 

  196. Liu X, Faes L, Kale AU, Wagner SK, Fu DJ, Bruynseels A, Denniston AK (2019) A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. The lancet digital health 1(6):e271–e297

    Google Scholar 

  197. Yin W, Li L, Wu FX (2022) Deep learning for brain disorder diagnosis based on fMRI images. Neurocomputing 469:332–345

    Google Scholar 

  198. Noor MBT, Zenia NZ, Kaiser MS, Mamun SA, Mahmud M (2020) Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia. Brain informatics 7:1–21

    Google Scholar 

  199. Ebrahimighahnavieh MA, Luo S, Chiong R (2020) Deep learning to detect Alzheimer’s disease from neuroimaging: A systematic literature review. Comput Methods Programs Biomed 187:105242

    Google Scholar 

  200. Craik A, He Y, Contreras-Vidal JL (2019) Deep learning for electroencephalogram (EEG) classification tasks: a review. J Neural Eng 16(3):031001

    Google Scholar 

  201. Kora P, Meenakshi K, Swaraja K, Rajani A, Raju MS (2021) EEG based interpretation of human brain activity during yoga and meditation using machine learning: A systematic review. Complement Ther Clin Pract 43:101329

    Google Scholar 

  202. Anwar SM, Majid M, Qayyum A, Awais M, Alnowami M, Khan MK (2018) Medical image analysis using convolutional neural networks: a review. J Med Syst 42:1–13

    Google Scholar 

  203. Yamashita R, Nishio M, Do RKG, Togashi K (2018) Convolutional neural networks: an overview and application in radiology. Insights Imaging 9:611–629

    Google Scholar 

  204. Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT press

  205. Bengio Y, Goodfellow I, Courville A (2017) Deep learning, vol 1. MIT press, Cambridge, MA, USA

    Google Scholar 

  206. Gulli A, Pal S (2017) Deep learning with Keras. Packt Publishing Ltd

  207. Ismail Fawaz H, Forestier G, Weber J, Idoumghar L, Muller PA (2019) Deep learning for time series classification: a review. Data Min Knowl Disc 33(4):917–963

    MathSciNet  Google Scholar 

  208. Längkvist M, Karlsson L, Loutfi A (2014) A review of unsupervised feature learning and deep learning for time-series modeling. Pattern Recogn Lett 42:11–24

    Google Scholar 

  209. Wang X, Wang X, Liu W, Chang Z, Kärkkäinen T, Cong F (2021) One dimensional convolutional neural networks for seizure onset detection using long-term scalp and intracranial EEG. Neurocomputing 459:212–222

    Google Scholar 

  210. Tan C, Sun F, Kong T, Zhang W, Yang C, Liu C (2018) A survey on deep transfer learning. In: Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4–7, 2018, Proceedings, Part III 27. Springer International Publishing, pp 270–279

  211. Zhuang F, Qi Z, Duan K, Xi D, Zhu Y, Zhu H, He Q (2020) A comprehensive survey on transfer learning. Proc IEEE 109(1):43–76

    Google Scholar 

  212. Morid MA, Borjali A, Del Fiol G (2021) A scoping review of transfer learning research on medical image analysis using ImageNet. Comput Biol Med 128:104115

    Google Scholar 

  213. Cheplygina V, de Bruijne M, Pluim JP (2019) Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis. Med Image Anal 54:280–296

    Google Scholar 

  214. Boveiri HR, Khayami R, Javidan R, Mehdizadeh A (2020) Medical image registration using deep neural networks: a comprehensive review. Comput Electr Eng 87:106767

    Google Scholar 

  215. Liu W, Wang Z, Liu X, Zeng N, Liu Y, Alsaadi FE (2017) A survey of deep neural network architectures and their applications. Neurocomputing 234:11–26

    Google Scholar 

  216. Nikhil Chandran A, Sreekumar K, Subha DP (2021) EEG-based automated detection of schizophrenia using long short-term memory (LSTM) network. In: Advances in Machine Learning and Computational Intelligence: Proceedings of ICMLCI 2019. Springer Singapore, pp 229–236

  217. Baur C, Denner S, Wiestler B, Navab N, Albarqouni S (2021) Autoencoders for unsupervised anomaly segmentation in brain MR images: a comparative study. Med Image Anal 69:101952

    Google Scholar 

  218. Wei R, Mahmood A (2020) Recent advances in variational autoencoders with representation learning for biomedical informatics: A survey. Ieee Access 9:4939–4956

    Google Scholar 

  219. Nayak DR, Padhy N, Mallick PK, Singh A (2022) A deep autoencoder approach for detection of brain tumor images. Comput Electr Eng 102:108238

    Google Scholar 

  220. Mallick PK, Ryu SH, Satapathy SK, Mishra S, Nguyen GN, Tiwari P (2019) Brain MRI image classification for cancer detection using deep wavelet autoencoder-based deep neural network. IEEE Access 7:46278–46287

    Google Scholar 

  221. Guo Z, Wu L, Li Y, Li B (2021) Deep neural network classification of EEG data in schizophrenia. In: 2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS). IEEE, pp 1322–1327

  222. Prabhakar SK, Lee SW (2022) SASDL and RBATQ: Sparse Autoencoder With Swarm Based Deep Learning and Reinforcement Based Q-Learning for EEG Classification. IEEE Open Journal of Engineering in Medicine and Biology 3:58–68

    Google Scholar 

  223. Ahmedt-Aristizabal D, Fernando T, Denman S, Robinson JE, Sridharan S, Johnston PJ, Fookes C (2020) Identification of children at risk of schizophrenia via deep learning and EEG responses. IEEE J Biomed Health Inform 25(1):69–76

    Google Scholar 

  224. Oh SL, Vicnesh J, Ciaccio EJ, Yuvaraj R, Acharya UR (2019) Deep convolutional neural network model for automated diagnosis of schizophrenia using EEG signals. Appl Sci 9(14):2870

    Google Scholar 

  225. Bondugula, R. K., Sivangi, K. B., & Udgata, S. K. (2022). Identification of schizophrenic individuals using activity records through visualization of recurrent networks. In Intelligent Systems: Proceedings of ICMIB 2021 (pp. 653–664). Singapore: Springer Nature Singapore

  226. Zülfikar A, Mehmet A (2022) Empirical mode decomposition and convolutional neural network-based approach for diagnosing psychotic disorders from eeg signals. Appl Intell 52(11):12103–12115

    Google Scholar 

  227. Khare SK, Bajaj V, Acharya UR (2021) SPWVD-CNN for automated detection of schizophrenia patients using EEG signals. IEEE Trans Instrum Meas 70:1–9

    Google Scholar 

  228. Chu, L., Qiu, R., Liu, H., Ling, Z., Zhang, T., & Wang, J. (2017). Individual recognition in schizophrenia using deep learning methods with random forest and voting classifiers: Insights from resting state EEG streams. arXiv preprint arXiv:1707.03467

  229. Phang CR, Noman F, Hussain H, Ting CM, Ombao H (2019) A multi-domain connectome convolutional neural network for identifying schizophrenia from EEG connectivity patterns. IEEE J Biomed Health Inform 24(5):1333–1343

    Google Scholar 

  230. Laksono IK, Imah EM (2021) Schizophrenia detection based on electroencephalogram using support vector machine. In: 2021 International Conference on ICT for Smart Society (ICISS). IEEE, pp 1–6

  231. Singh K, Singh S, Malhotra J (2021) Spectral features based convolutional neural network for accurate and prompt identification of schizophrenic patients. Proc Inst Mech Eng [H] 235(2):167–184

    Google Scholar 

  232. Ellis CA, Sattiraju A, Miller R, Calhoun V (2022) Examining effects of schizophrenia on EEG with explainable deep learning models. In: 2022 IEEE 22nd International Conference on Bioinformatics and Bioengineering (BIBE). IEEE, pp 301–304

  233. Wu, Y., Xia, M., Wang, X., Zhang, Y. (2023). Schizophrenia detection based on EEG using recurrent auto-encoder framework. In Neural Information Processing: 29th International Conference, ICONIP 2022 Virtual Event, November 22–26, 2022, Proceedings, Part II Springer International Publishing Cham 62 73

  234. Menon, N. G., Bhavana, N. D., & Hema, D. D. Towards better intelligent implementation of Schizophrenia prediction using federated deep learning framework. framework, 6(S3), 5631–5645

  235. Siuly S, Li Y, Wen P, Alcin OF (2022) SchizoGoogLeNet: the googlenet-based deep feature extraction design for automatic detection of schizophrenia. Comput Intell Neurosci 2022:1–13

    Google Scholar 

  236. Ko DW, Yang JJ (2022) EEG-Based schizophrenia diagnosis through time series image conversion and deep learning. Electronics 11(14):2265

    Google Scholar 

  237. Kose, M., Ahirwal, M. K., & Atulkar, M. (2022). Weighted Ordinal Connection based Functional Network Classification for Schizophrenia Disease Detection using EEG signal

  238. Supakar R, Satvaya P, Chakrabarti P (2022) A deep learning based model using RNN-LSTM for the Detection of Schizophrenia from EEG data. Comput Biol Med 151:106225. https://doi.org/10.1016/j.compbiomed.2022.106225

  239. Aslan Z, Akin M (2022) A deep learning approach in automated detection of schizophrenia using scalogram images of EEG signals. Physical and Engineering Sciences in Medicine 45(1):83–96

    Google Scholar 

  240. Bagherzadeh S, Shahabi MS, Shalbaf A (2022) Detection of schizophrenia using hybrid of deep learning and brain effective connectivity image from electroencephalogram signal. Comput Biol Med 146:105570

    Google Scholar 

  241. Lillo E, Mora M, Lucero B (2022) Automated diagnosis of schizophrenia using EEG microstates and Deep Convolutional Neural Network. Expert Syst Appl 209:118236

    Google Scholar 

  242. Sharma G, Joshi AM (2022) SzHNN: A Novel and Scalable Deep Convolution Hybrid Neural Network Framework for Schizophrenia Detection Using Multichannel EEG. IEEE Trans Instrum Meas 71:1–9

    Google Scholar 

  243. Wang Z, Feng J, Jiang R, Shi Y, Li X, Xue R, Deng W (2022) Automated Rest EEG-Based Diagnosis of Depression and Schizophrenia Using a Deep Convolutional Neural Network. IEEE Access 10:104472–104485

    Google Scholar 

  244. Jindal, K., Upadhyay, R., Padhy, P. K., & Longo, L. (2022). Bi-LSTM-deep CNN for schizophrenia detection using MSST-spectral images of EEG signals. In Artificial Intelligence-Based Brain-Computer Interface (pp. 145–162). Academic Press

  245. Korda AI, Ventouras E, Asvestas P, Toumaian M, Matsopoulos GK, Smyrnis N (2022) Convolutional neural network propagation on electroencephalographic scalograms for detection of schizophrenia. Clin Neurophysiol 139:90–105

    Google Scholar 

  246. Nsugbe, E., Samuel, O. W., Asogbon, M. G., & Li, G. (2022, June). Intelligence combiner: a combination of deep learning and handcrafted features for an adolescent psychosis prediction using EEG signals. In 2022 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4. 0&IoT) (pp. 92–97). IEEE

  247. Prabhakar SK, Lee SW (2022) Improved sparse representation based robust hybrid feature extraction models with transfer and deep learning for EEG classification. Expert Syst Appl 198:116783

    Google Scholar 

  248. Sun J, Cao R, Zhou M, Hussain W, Wang B, Xue J, Xiang J (2021) A hybrid deep neural network for classification of schizophrenia using EEG Data. Sci Rep 11(1):1–16

    Google Scholar 

  249. Naira, C. A. T., & Jos, C. (2019). Classification of people who suffer schizophrenia and healthy people by EEG signals using deep learning. International Journal of Advanced Computer Science and Applications, 10(10)

  250. Barros C, Roach B, Ford JM, Pinheiro AP, Silva CA (2022) From sound perception to automatic detection of schizophrenia: an EEG-based deep learning approach. Front Psych 12:2659

    Google Scholar 

  251. Sharma, G., & Joshi, A. M. (2021). Novel eeg based schizophrenia detection with iomt framework for smart healthcare. arXiv preprint arXiv:2111.11298

  252. Hassan F, Hussain SF, Qaisar SM (2023) Fusion of multivariate EEG signals for schizophrenia detection using CNN and machine learning techniques. Information Fusion 92:466–478

    Google Scholar 

  253. Khare, S. K., Bajaj, V., & Acharya, U. R. (2023). SchizoNET: a robust and accurate Margenau-Hill time-frequency distribution based deep neural network model for schizophrenia detection using EEG signals. Physiological Measurement

  254. Luján, M. Á., Mateo Sotos, J., Torres, A., Santos, J. L., Quevedo, O., & Borja, A. L. (2022). Mental Disorder Diagnosis from EEG Signals Employing Automated Leaning Procedures Based on Radial Basis Functions. Journal of Medical and Biological Engineering, 1–7

  255. Siuly, S., Guo, Y., Alcin, O. F., Li, Y., Wen, P., & Wang, H. (2023). Exploring deep residual network based features for automatic schizophrenia detection from EEG. Physical and Engineering Sciences in Medicine, 1–14

  256. Li, B., Wang, J., Guo, Z., & Li, Y. (2023). Automatic detection of schizophrenia based on spatial–temporal feature mapping and LeViT with EEG signals. Expert Systems with Applications, 119969

  257. Grover N, Chharia A, Upadhyay R, Longo L (2023) Schizo-Net: A novel Schizophrenia Diagnosis framework using late fusion multimodal deep learning on Electroencephalogram-based Brain connectivity indices. IEEE Trans Neural Syst Rehabilitation Eng 31:464–473. https://doi.org/10.1109/tnsre.2023.3237375

    Article  Google Scholar 

  258. Göker, H. (2023). 1D-convolutional neural network approach and feature extraction methods for automatic detection of schizophrenia. Signal, Image and Video Processing, 1–10

  259. Tekgul H, Bourgeois BF, Gauvreau K, Bergin AM (2005) Electroencephalography in neonatal seizures: comparison of a reduced and a full 10/20 montage. Pediatr Neurol 32(3):155–161

    Google Scholar 

  260. Uludağ K, Roebroeck A (2014) General overview on the merits of multimodal neuroimaging data fusion. Neuroimage 102:3–10

    Google Scholar 

  261. Biessmann F, Plis S, Meinecke FC, Eichele T, Muller KR (2011) Analysis of multimodal neuroimaging data. IEEE Rev Biomed Eng 4:26–58. https://doi.org/10.1109/rbme.2011.2170675

    Article  Google Scholar 

  262. Ewers M, Frisoni GB, Teipel SJ, Grinberg LT, Amaro E Jr, Heinsen H, Hampel H (2011) Staging Alzheimer’s disease progression with multimodality neuroimaging. Prog Neurobiol 95(4):535–546

    Google Scholar 

  263. Aine CJ, Bockholt HJ, Bustillo JR, Cañive JM, Caprihan A, Gasparovic C, Calhoun VD (2017) Multimodal neuroimaging in schizophrenia: description and dissemination. Neuroinformatics 15:343–364

    Google Scholar 

  264. Sui J, Pearlson GD, Du Y, Yu Q, Jones TR, Chen J, Calhoun VD (2015) In search of multimodal neuroimaging biomarkers of cognitive deficits in schizophrenia. Biological psychiatry 78(11):794–804

    Google Scholar 

  265. Panigrahy C, Seal A, Gonzalo-Martín C, Pathak P, Jalal AS (2023) Parameter adaptive unit-linking pulse coupled neural network based MRI–PET/SPECT image fusion. Biomed Signal Process Control 83:104659

    Google Scholar 

  266. Cichy RM, Oliva A (2020) AM/EEG-fMRI fusion primer: resolving human brain responses in space and time. Neuron 107(5):772–781

    Google Scholar 

  267. Lecaignard F, Bertrand O, Caclin A, Mattout J (2021) Empirical Bayes evaluation of fused EEG-MEG source reconstruction: Application to auditory mismatch evoked responses. Neuroimage 226:117468. https://doi.org/10.1016/j.neuroimage.2020.117468

  268. Liu, R., Huang, Z. A., Hu, Y., Zhu, Z., Wong, K. C., & Tan, K. C. (2022). Attention-Like Multimodality Fusion With Data Augmentation for Diagnosis of Mental Disorders Using MRI. IEEE Transactions on Neural Networks and Learning Systems

  269. Du Y, He X, Kochunov P, Pearlson G, Hong LE, van Erp TG, Calhoun VD (2022) A new multimodality fusion classification approach to explore the uniqueness of schizophrenia and autism spectrum disorder. Hum Brain Mapp 43(12):3887–3903

    Google Scholar 

  270. Tikàsz A, Dumais A, Lipp O, Stip E, Lalonde P, Laurelli M, Potvin S (2019) Reward-related decision-making in schizophrenia: A multimodal neuroimaging study. Psychiatry Research: Neuroimaging 286:45–52

    Google Scholar 

  271. Lemley J, Bazrafkan S, Corcoran P (2017) Deep Learning for Consumer Devices and Services: Pushing the limits for machine learning, artificial intelligence, and computer vision. IEEE Consumer Electronics Magazine 6(2):48–56

    Google Scholar 

  272. Pramod, A., Naicker, H. S., & Tyagi, A. K. (2021). Machine learning and deep learning: Open issues and future research directions for the next 10 years. Computational analysis and deep learning for medical care: Principles, methods, and applications, 463–490

  273. Janiesch C, Zschech P, Heinrich K (2021) Machine learning and deep learning. Electron Mark 31(3):685–695. https://doi.org/10.1007/s12525-021-00475-2

    Article  Google Scholar 

  274. Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, Dean J (2019) A guide to deep learning in healthcare. Nat Med 25(1):24–29

    Google Scholar 

  275. Faust O, Hagiwara Y, Hong TJ, Lih OS, Acharya UR (2018) Deep learning for healthcare applications based on physiological signals: A review. Comput Methods Programs Biomed 161:1–13

    Google Scholar 

  276. Pang G, Shen C, Cao L, Hengel AVD (2021) Deep learning for anomaly detection: A review. ACM computing surveys (CSUR) 54(2):1–38

    Google Scholar 

  277. Lan K, Wang DT, Fong S, Liu LS, Wong KK, Dey N (2018) A survey of data mining and deep learning in bioinformatics. J Med Syst 42:1–20

    Google Scholar 

  278. Chollet, F. (2017). The limitations of deep learning. Deep learning with Python

  279. de Santana Correia A, Colombini EL (2022) Attention, please! A survey of neural attention models in deep learning. Artif Intell Rev 55(8):6037–6124

    Google Scholar 

  280. Ding K, Xu Z, Tong H, Liu H (2022) Data augmentation for deep graph learning: A survey. ACM SIGKDD Explorations Newsl 24(2):61–77

    Google Scholar 

  281. Zhang H, Liang W, Li C, Xiong Q, Shi H, Hu L, Li G (2022) DCML: deep contrastive mutual learning for COVID-19 recognition. Biomed Signal Process Control 77:103770

    Google Scholar 

  282. Ahmed I, Jeon G, Piccialli F (2022) From artificial intelligence to explainable artificial intelligence in industry 4.0 a survey on what, how, and where IEEE. Transactions on Industrial Informatics 18(8):5031–5042

    Google Scholar 

  283. Novakovsky G, Dexter N, Libbrecht MW, Wasserman WW, Mostafavi S (2023) Obtaining genetics insights from deep learning via explainable artificial intelligence. Nat Rev Genet 24(2):125–137

    Google Scholar 

  284. Loh, H. W., Ooi, C. P., Seoni, S., Barua, P. D., Molinari, F., & Acharya, U. R. (2022). Application of explainable artificial intelligence for healthcare: A systematic review of the last decade (2011–2022). Computer Methods and Programs in Biomedicine, 107161

  285. Van der Velden, B. H., Kuijf, H. J., Gilhuijs, K. G., & Viergever, M. A. (2022). Explainable artificial intelligence (XAI) in deep learning-based medical image analysis. Medical Image Analysis, 102470

  286. Lauriola I, Lavelli A, Aiolli F (2022) An introduction to deep learning in natural language processing: Models, techniques, and tools. Neurocomputing 470:443–456

    Google Scholar 

  287. Gunny A, Rankin D, Krupa J, Saleem M, Nguyen T, Coughlin M, Holzman B (2022) Hardware-accelerated inference for real-time gravitational-wave astronomy. Nature Astronomy 6(5):529–536

    Google Scholar 

  288. Boukhennoufa I, Zhai X, Utti V, Jackson J, McDonald-Maier KD (2022) Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review. Biomed Signal Process Control 71:103197

    Google Scholar 

  289. Luvizutto GJ, Silva GF, Nascimento MR, Sousa Santos KC, Appelt PA, de Moura Neto E, Bazan R (2022) Use of artificial intelligence as an instrument of evaluation after stroke: a scoping review based on international classification of functioning, disability and health concept: AI applications for stroke evaluation. Top Stroke Rehabil 29(5):331–346

    Google Scholar 

  290. Kohli V, Tripathi U, Chamola V, Rout BK, Kanhere SS (2022) A review on Virtual Reality and Augmented Reality use-cases of Brain Computer Interface based applications for smart cities. Microprocess Microsyst 88:104392

    Google Scholar 

  291. Aggarwal, S., & Chugh, N. (2022). Review of machine learning techniques for EEG based brain computer interface. Archives of Computational Methods in Engineering, 1–20

  292. Ciccarelli G, Federico G, Mele G, Di Cecca A, Migliaccio M, Ilardi CR, Cavaliere C (2023) Simultaneous real-time EEG-fMRI neurofeedback: A systematic review. Front Hum Neurosci 17:132

    Google Scholar 

  293. Vos, G., Trinh, K., Sarnyai, Z., & Azghadi, M. R. (2023). Generalizable Machine Learning for Stress Monitoring from Wearable Devices: A Systematic Literature Review. International Journal of Medical Informatics, 105026

  294. Al-Turjman F, Baali I (2022) Machine learning for wearable IoT-based applications: A survey. Transactions on Emerging Telecommunications Technologies 33(8):e3635

    Google Scholar 

  295. Francese R, Yang X (2022) Supporting autism spectrum disorder screening and intervention with machine learning and wearables: a systematic literature review. Complex & Intelligent Systems 8(5):3659–3674

    Google Scholar 

  296. Sujith AVLN, Sajja GS, Mahalakshmi V, Nuhmani S, Prasanalakshmi B (2022) Systematic review of smart health monitoring using deep learning and Artificial intelligence. Neuroscience Informatics 2(3):100028

    Google Scholar 

  297. Houssein EH, Hammad A, Ali AA (2022) Human emotion recognition from EEG-based brain–computer interface using machine learning: a comprehensive review. Neural Comput Appl 34(15):12527–12557

    Google Scholar 

  298. Noble WS (2006) What is a support vector machine? Nat Biotechnol 24(12):1565–1567

    Google Scholar 

  299. Zhu D, Lu S, Wang M, Lin J, Wang Z (2020) Efficient precision-adjustable architecture for softmax function in deep learning. IEEE Trans Circuits Syst II Express Briefs 67(12):3382–3386. https://doi.org/10.1109/tcsii.2020.3002564

    Article  Google Scholar 

  300. Gallagher BJ III, Jones BJ, McFalls JA Jr, Pisa AM (2006) Social class and type of schizophrenia. Eur Psychiatry 21(4):233–237

    Google Scholar 

  301. Cimr D, Busovsky D, Fujita H, Studnicka F, Cimler R, Hayashi T (2023) Classification of health deterioration by geometric invariants. Comput Methods Programs Biomed 239:107623

    Google Scholar 

  302. Cimr D, Fujita H, Busovsky D, Cimler R (2024) Enhancing EEG signal analysis with geometry invariants for multichannel fusion. Information Fusion 102:102023

    Google Scholar 

  303. Alonso JF, Romero S, Ballester MR, Antonijoan RM, Mañanas MA (2015) Stress assessment based on EEG univariate features and functional connectivity measures. Physiol Meas 36(7):1351–1365. https://doi.org/10.1088/0967-3334/36/7/1351

    Article  Google Scholar 

  304. Moon SE, Chen CJ, Hsieh CJ, Wang JL, Lee JS (2020) Emotional EEG classification using connectivity features and convolutional neural networks. Neural Netw 132:96–107. https://doi.org/10.1016/j.neunet.2020.08.009

    Article  Google Scholar 

  305. Lee YY, Hsieh S (2014) Classifying different emotional states by means of EEG-based functional connectivity patterns. PLoS ONE 9(4):e95415

    Google Scholar 

  306. Mirzaei S, Ghasemi P (2021) EEG motor imagery classification using dynamic connectivity patterns and convolutional autoencoder. Biomed Signal Process Control 68:102584. https://doi.org/10.1016/j.bspc.2021.102584

  307. Tafreshi TF, Daliri MR, Ghodousi M (2019) Functional and effective connectivity based features of EEG signals for object recognition. Cogn Neurodyn 13:555–566

    Google Scholar 

  308. Arunkumar N, Kumar KR, Venkataraman V (2018) Entropy features for focal EEG and non focal EEG. Journal of computational science 27:440–444

    Google Scholar 

  309. Tan S (2006) An effective refinement strategy for KNN text classifier. Expert Syst Appl 30(2):290–298. https://doi.org/10.1016/j.eswa.2005.07.019

    Article  Google Scholar 

  310. Pal M (2005) Random forest classifier for remote sensing classification. Int J Remote Sens 26(1):217–222. https://doi.org/10.1080/01431160412331269698

    Article  Google Scholar 

  311. Jang JS (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685

    Google Scholar 

  312. Quinlan JR (1996) Learning decision tree classifiers. ACM Computing Surveys (CSUR) 28(1):71–72

    Google Scholar 

  313. Vafaeikia, P., Namdar, K., & Khalvati, F. (2020). A brief review of deep multi-task learning and auxiliary task learning. arXiv preprint arXiv:2007.01126

  314. Yang Q, Liu Y, Chen T, Tong Y (2019) Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST) 10(2):1–19. https://doi.org/10.1145/3298981

    Article  Google Scholar 

  315. Abdar M, Pourpanah F, Hussain S, Rezazadegan D, Liu L, Ghavamzadeh M, Nahavandi S (2021) A review of uncertainty quantification in deep learning: Techniques, applications and challenges. Information Fusion 76:243–297

    Google Scholar 

  316. Brauwers G, Frasincar F (2023) A general survey on attention mechanisms in deep learning. IEEE Trans Knowl Data Eng 35(4):3279–3298. https://doi.org/10.1109/tkde.2021.3126456

    Article  Google Scholar 

  317. Hafiz, A. M., Parah, S. A., & Bhat, R. U. A. (2021). Attention mechanisms and deep learning for machine vision: A survey of the state of the art. arXiv preprint arXiv:2106.07550

  318. Liu H, Chatterjee I, Zhou M, Lu XS, Abusorrah A (2020) Aspect-based sentiment analysis: A survey of deep learning methods. IEEE Transactions on Computational Social Systems 7(6):1358–1375

    Google Scholar 

  319. Lin, T., Wang, Y., Liu, X., & Qiu, X. (2022). A survey of transformers. AI Open

  320. Guo D, Shao Y, Cui Y, Wang Z, Zhang L, Shen C (2021) Graph attention tracking. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 9543–9552

  321. Chen T, Li X, Yin H, Zhang J (2018) Call attention to rumors: Deep attention based recurrent neural networks for early rumor detection. In: Trends and Applications in Knowledge Discovery and Data Mining: PAKDD 2018 Workshops, BDASC, BDM, ML4Cyber, PAISI, DaMEMO, Melbourne, VIC, Australia, June 3, 2018, Revised Selected Papers 22. Springer International Publishing, pp 40–52

  322. Xue G, Liu S, Ma Y (2020) A hybrid deep learning-based fruit classification using attention model and convolution autoencoder. Complex Intell Syst 9(3):2209–2219. https://doi.org/10.1007/s40747-020-00192-x

    Article  Google Scholar 

  323. Wu, Z., Pan, S., Long, G., Jiang, J., & Zhang, C. (2019). Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121

  324. Georgousis S, Kenning MP, Xie X (2021) Graph deep learning: State of the art and challenges. IEEE Access 9:22106–22140

    Google Scholar 

  325. Prabhu, A., Varma, G., & Namboodiri, A. (2018). Deep expander networks: Efficient deep networks from graph theory. In Proceedings of the European Conference on Computer Vision (ECCV) (pp. 20–35)

  326. Wang M, El-Fiqi H, Hu J, Abbass HA (2019) Convolutional neural networks using dynamic functional connectivity for EEG-based person identification in diverse human states. IEEE Trans Inf Forensics Secur 14(12):3259–3272

    Google Scholar 

  327. Zhang, G., Yu, M., Liu, Y. J., Zhao, G., Zhang, D., & Zheng, W. (2021). SparseDGCNN: Recognizing emotion from multichannel EEG signals. IEEE Transactions on Affective Computing

  328. Song T, Zheng W, Song P, Cui Z (2018) EEG emotion recognition using dynamical graph convolutional neural networks. IEEE Trans Affect Comput 11(3):532–541

    Google Scholar 

  329. Pan, S., Hu, R., Long, G., Jiang, J., Yao, L., & Zhang, C. (2018). Adversarially regularized graph autoencoder for graph embedding. arXiv preprint arXiv:1802.04407

  330. Guo K, Hu Y, Qian Z, Liu H, Zhang K, Sun Y, Yin B (2020) Optimized graph convolution recurrent neural network for traffic prediction. IEEE Transactions on Intelligent Transportation Systems 22(2):1138–1149

    Google Scholar 

  331. Liu P, Qiu X, Huang X (2016) Deep multi-task learning with shared memory. arXiv preprint arXiv:1609.07222

  332. Huang H, Yang G, Zhang W, Xu X, Yang W, Jiang W, Lai X (2021) A deep multi-task learning framework for brain tumor segmentation. Front Oncol 11:690244

    Google Scholar 

  333. Zeng, N., Li, H., & Peng, Y. (2021). A new deep belief network-based multi-task learning for diagnosis of Alzheimer’s disease. Neural Computing and Applications, 1–12

  334. Roy, A. G., Siddiqui, S., Pölsterl, S., Navab, N., & Wachinger, C. (2019). Braintorrent: A peer-to-peer environment for decentralized federated learning. arXiv preprint arXiv:1905.06731

  335. Yuan, B., Ge, S., & Xing, W. (2020). A federated learning framework for healthcare iot devices. arXiv preprint arXiv:2005.05083

  336. Połap D, Srivastava G, Yu K (2021) Agent architecture of an intelligent medical system based on federated learning and blockchain technology. J Inf Secur Appl 58:102748. https://doi.org/10.1016/j.jisa.2021.102748

  337. Li G, Wu G, Xu G, Li C, Zhu Z, Ye Y, Zhang H (2023) Pathological image classification via embedded fusion mutual learning. Biomed Signal Process Control 79:104181. https://doi.org/10.1016/j.bspc.2022.104181

  338. Yang Q, Geng C, Chen R, Pang C, Han R, Lyu L, Zhang Y (2022) DMU-Net: Dual-route mirroring U-Net with mutual learning for malignant thyroid nodule segmentation. Biomed Signal Process Control 77:103805

    Google Scholar 

  339. Khare, S. K., March, S., Barua, P. D., Gadre, V. M., & Acharya, U. R. (2023). Application of data fusion for automated detection of children with developmental and mental disorders: A systematic review of the last decade. Information Fusion, 101898

  340. Yaacob, H., Hossain, F., Shari, S., Khare, S. K., Ooi, C. P., & Acharya, U. R. (2023). Application of Artificial Intelligence Techniques for Brain-Computer Interface in Mental Fatigue Detection: A Systematic Review (2011–2022). IEEE Access

  341. Khare SK, Acharya UR (2023) Adazd-Net: Automated adaptive and explainable Alzheimer’s disease detection system using EEG signals. Knowl-Based Syst 278:110858

    Google Scholar 

  342. Khare SK, Acharya UR (2023) An explainable and interpretable model for attention deficit hyperactivity disorder in children using EEG signals. Comput Biol Med 155:106676

    Google Scholar 

  343. Górriz JM, Álvarez-Illán I, Álvarez-Marquina A, Arco JE, Atzmueller M, Ballarini F, Ferrández-Vicente JM (2023) Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends. Inf Fusion 100:101945. https://doi.org/10.1016/j.inffus.2023.101945

  344. Caldeira J, Nord B (2020) Deeply uncertain: comparing methods of uncertainty quantification in deep learning algorithms. Machine Learning: Science and Technology 2(1):015002

    Google Scholar 

  345. Zhu Y, Zabaras N, Koutsourelakis PS, Perdikaris P (2019) Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data. J Comput Phys 394:56–81

    MathSciNet  Google Scholar 

  346. Hu R, Fang F, Pain CC, Navon IM (2019) Rapid spatio-temporal flood prediction and uncertainty quantification using a deep learning method. J Hydrol 575:911–920. https://doi.org/10.1016/j.jhydrol.2019.05.087

    Article  Google Scholar 

  347. Shawahna A, Sait SM, El-Maleh A (2018) FPGA-based accelerators of deep learning networks for learning and classification. A review. ieee Access 7:7823–7859

    Google Scholar 

  348. Wang C, Gong L, Yu Q, Li X, Xie Y, Zhou X (2016) DLAU: A scalable deep learning accelerator unit on FPGA. IEEE Trans Comput Aided Des Integr Circuits Syst 36(3):513–517

    Google Scholar 

  349. Manasi, S. D., & Sapatnekar, S. S. (2021, January). DeepOpt: Optimized scheduling of CNN workloads for ASIC-based systolic deep learning accelerators. In Proceedings of the 26th Asia and South Pacific Design Automation Conference (pp. 235–241)

  350. Guo, Y. (2018). A survey on methods and theories of quantized neural networks. arXiv preprint arXiv:1808.04752

  351. Gupta M, Agrawal P (2022) Compression of deep learning models for text: A survey. ACM Transactions on Knowledge Discovery from Data (TKDD) 16(4):1–55

    Google Scholar 

  352. Alzu’bi A, Amira A, Ramzan N (2017) Content-based image retrieval with compact deep convolutional features. Neurocomputing 249:95–105

    Google Scholar 

  353. Eren L, Ince T, Kiranyaz S (2019) A generic intelligent bearing fault diagnosis system using compact adaptive 1D CNN classifier. Journal of Signal Processing Systems 91:179–189

    Google Scholar 

Download references

Acknowledgements

This research is part of the PID2022-137451OB-I00 project funded by the CIN/AEI/https://doi.org/10.13039/501100011033 and by FSE+.

Author information

Authors and Affiliations

  1. Internship in BioMedical Machine Learning Lab, The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW, 2052, Australia

    Mahboobeh Jafari & Afshin Shoeibi

  2. Data Science and Computational Intelligence Institute, University of Granada, Granada, Spain

    Mahboobeh Jafari, Delaram Sadeghi, Afshin Shoeibi, David López García & Juan M. Gorriz

  3. BioMedical Machine Learning Lab, The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW, 2052, Australia

    Hamid Alinejad-Rokny

  4. UNSW Data Science Hub, The University of New South Wales, Sydney, NSW, 2052, Australia

    Hamid Alinejad-Rokny

  5. Health Data Analytics Program, Centre for Applied Artificial Intelligence, Macquarie University, Sydney, 2109, Australia

    Hamid Alinejad-Rokny

  6. Data Science Lab, School of Computing, Macquarie University, Sydney, NSW, 2109, Australia

    Amin Beheshti

  7. Monash Biomedical Imaging (MBI), Monash University, Melbourne, Australia

    Zhaolin Chen

  8. School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, Australia

    U. Rajendra Acharya

  9. Department of Psychiatry, University of Cambridge, Cambridge, UK

    Juan M. Gorriz

Authors
  1. Mahboobeh Jafari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Delaram Sadeghi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Afshin Shoeibi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hamid Alinejad-Rokny
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Amin Beheshti
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. David López García
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Zhaolin Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. U. Rajendra Acharya
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Juan M. Gorriz
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Mahboobeh Jafari: Data curation; funding acquisition; investigation; writing – original draft; writing – review and editing.

Delaram Sadeghi: formal analysis; investigation; visualization; writing – original draft; writing – review and editing.

Afshin Shoeibi: Conceptualization; formal analysis; investigation; project administration; resources; supervision; visualization; writing – original draft; writing – review and editing.

Hamid Alinejad-Rokny: Formal analysis; supervision; validation; writing – review and editing.

Amin Beheshti: Formal analysis; methodology; validation; writing – review and editing.

David López García: writing – review and editing.

Zhaolin Chen: formal analysis; investigation; validation; writing – review and editing.

U. Rajendra Acharya: Conceptualization; formal analysis; investigation; methodology; supervision; validation; writing – original draft; writing – review and editing.

Juan Manuel Gorriz: Conceptualization; investigation; project administration; resources; supervision; validation; visualization; writing – original draft; writing – review and editing.

Corresponding authors

Correspondence to Afshin Shoeibi or Hamid Alinejad-Rokny.

Ethics declarations

Ethical and informed consent

This is a review paper and we do not use any data.

Conflicts of interest

The authors declare no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Mahboobeh Jafari and Delaram Sadeghi contributed equally to this work.

Appendices

Appendix 1

Accuracy can be defined as the proportion of correctly predicted observations to the total number of observations [92].

$$Acc=\frac{TP+TN}{FP+FN+TP+TN}$$

Sensitivity, also referred to as recall, can be defined as the proportion of correctly predicted positive observations to the total number of cases that have a particular condition of interest [92].

$$Sen=\frac{TP}{FN+TP}$$

Specificity can be defined as the proportion of correctly predicted negative observations to the total number of observations that are negative [92].

$$Spec=\frac{TN}{FN+TN}$$

Precision, also known as positive predictive value, represents the proportion of correctly predicted positive observations to the total number of observations that are predicted as positive [92].

$$Prec=\frac{TP}{TP+FP}$$

Appendix 2: Abbreviations

A

Absolute value of the highest slope of autoregressive coefficients (AVLSAC)

Accuracy (Acc)

Adaptive neuro-fuzzy inference system (ANFIS)

Alzheimer's disease (AD)

Analysis of Variance (ANOVA)

Application-specific integrated circuits (ASIC)

Approximate Entropy (ApEn)

Artificial intelligence (AI)

Artificial neural networks (ANNs)

Autoencoders (AEs)

Autoregressive (AR)

B

Back propagation network (BPN)

Bat optimization (BA)

Black Hole (BH)

Boosted version of Direct Linear Discriminant Analysis (BDLDA)

Brain-computer interface (BCI)

C

Clinically High-risk (CHR)

Cognitive-behavioral therapy (CBT)

Complex Network (CN)

Computed tomography (CT)

Computer-aided diagnosis system (CADS)

Continuous wavelet transform (CWT)

Convolutional AE (CAE)

Convolutional neural networks (CNNs)

Correlation-based feature selection (CBFS)

Cyclic Group of Prime Order Pattern (CGP17Pat)

D

Data augmentation (DA)

Decision tree (DT)

Deep attention mechanisms (DAMs)

Deep brain stimulation (DBS)

Deep learning (DL)

Deep multi-task learning (DMTL)

Deep mutual learning (DML)

Detrend Fluctuation Analysis (DFA)

Diagnostic and statistical manual of mental disorders (DSM)

Diffusion tensor imaging (DTI)

Discrete Fourier transform (DFT)

Discrete wavelet transform (DWT)

E

Electroconvulsive therapy (ECT)

Electroencephalography (EEG)

Electromyogram (EMG)

Empirical mode decomposition (EMD)

Ensemble bagged tree (EBT)

Event-related potentials (ERPs)

Empirical wavelet transform (EWT)

Expectation Maximization based Principal Component Analysis (EM-PCA)

Explainable AI (XAI)

Extreme learning machine (ELM)

F

Fast Fourier transform (FFT)

Feature ranking (FR)

Federated learning (FL)

Field programmable gate arrays (FPGA)

Flexible least square support vector machine (F-LSSVM)

Fractal Dimension (FD)

Fully connected (FC)

Functional magnetic resonance imaging (fMRI)

Fuzzy C-Means (FCM)

Fuzzy synchronization likelihood (FSL)

G

Gated recurrent unit (GRU)

Genetic Algorithm (GA)

Graph AEs (GAEs)

Graph RNNs (GRNNs)

Graph Attention Networks (GATs)

Graph Convolutional Neural Networks (GCNN)

Graphics processing units (GPUs)

Grey-Wolf optimization (GSO)

H

Healthy control (HC)

Higuchi’s Fractal Dimension (HFD)

Hilbert Spectrum (HS)

Histogram of local variance (HLV)

Hurst Exponent (HE)

I

Independent component analysis (ICA)

Information Entropy (InEn)

Intrinsic Mode Functions (IMF)

Iterative neighborhood component analysis (INCA)

Iterative tunable q-factor wavelet transform (ITQWT)

J

K

K-nearest neighbors (KNN)

Kolmogorov Complexity (KOL)

Kruskal Wallis (KW)

L

Largest Lyapunov Exponent (LLE)

Lempel Ziv Complexity (LZC)

Linear discriminant analysis (LDA)

Linear predictive coding (LPC)

Linear series decomposition learner (LSDL)

Local binary pattern (LBP)

Logistic regression (LR)

Look Ahead Pattern (LAP)

long-short-term memory (LSTM)

Lyapunov exponents (Lya)

M

Machine learning (ML)

Magnetic resonance spectroscopy (MRS)

Magnetoencephalography (MEG)

maximum absolute pooling (MAP)

Mean Spectral Amplitude (MSA)

Mental Health Research Center (MHRC)

Multi-Channel Frequency Network (MUCHf-Net)

Multi-class Spatial Pattern of the Network (MSPN)

Multi-domain Connectome CNN (MDC-CNN)

Multi-Layer Perceptron (MLP)

Multi-level Discrete Wavelet Transformation (MDWT)

Multiple sclerosis (MS)

Multiscale principal component analysis (MSPCA)

Multisynchrosqueezing transform (MSST)

Multi-variate empirical mode decomposition (MEMD)

N

O

Optimized extreme learning machine (OELM)

P

Parkinson's disease (PD)

Partial directed coherence (PDC)

Partial Least Squares Non linear Regression (PLS-NLR)

Phase lag index (PLI)

Phase synchronization (PS)

Positron emission tomography (PET)

Power spectral density (PSD)

Precision (Pre)

Preferred reporting items for systematic reviews and meta-analyses (PRISMA)

Principal component analysis (PCA)

Probabilistic neural network (PNN)

Q

R

Radial Basis Function (RBF)

Random Forest (RF)

Random Subset Feature Selection (RSFS)

Recall (Re)

Recurrence Quantification Analysis (RQA)

Recurrent Auto-encoder (RAE)

Recurrent neural networks (RNNs)

Recursive feature elimination (RFE)

Robust variational mode decomposition (RVMD)

S

Schizophrenia (SZ)

Sensitivity (Sen)

Sequential forward selection (SFS)

Shannon entropy (ShEn)

Short-time Fourier transforms (STFT)

Signal-to-noise ratio (SNR)

Single-photon emission computerized tomography (SPECT)

Smoothed pseudo-Wigner–Ville distribution (SPWVD)

Sparse Autoencoder (SAE)

Specificity (Spe)

Spectral eEntropy (SpEn)

Squeeze Excitation Network-LSTM- Softmax (SLS)

Structural magnetic resonance imaging (sMRI)

Support Vector Machine (SVM)

Symbolic Transfer Entropy (STE)

Symmetrically weighted local binary patterns (SLBP)

Synchronization likelihood (SL)

T

t-distributed stochastic neighbor embedding (t-SNE)

Time–frequency representation (TFR)

Transcranial direct current stimulation (tDCS)

Transcranial magnetic stimulation (TMS)

Transfer Entropy (TE)

Tunable Q-factor wavelet transform (TQWT)

U

Uncertainty quantification (UQ)

V

Vector autoregressive (VAR)

W

Wavelet-enhanced Independent Component Analysis (wICA)

Wavelet Scattering Transform (WST)

Wavelet transform (WT)

Wolf-Bat Algorithm (WBA)

X

Y

Z

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jafari, M., Sadeghi, D., Shoeibi, A. et al. Empowering precision medicine: AI-driven schizophrenia diagnosis via EEG signals: A comprehensive review from 2002–2023. Appl Intell 54, 35–79 (2024). https://doi.org/10.1007/s10489-023-05155-6

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-023-05155-6

Keywords

Search

Navigation