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REVIEW 5 major objections 5 minor 176 references

EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks: A review

T0 review · 5 major / 5 minor · reviewed 2026-08-27 · deepseek-v4-flash

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Pith's one-line read This review claims to be the first comprehensive survey of neural-network approaches that detect major depressive disorder and bipolar disorder from EEG, reporting that deep learning generally outperforms shallow networks.

desk verdict Useful compilation, but the systematic-review scaffold is unreliable and the headline claim that deep learning outperforms shallow networks is contradicted by the paper's own table. read the letter →

arxiv 2009.13402 v2 pith:DGKNJDSB submitted 2020-09-28 q-bio.NC cs.LGeess.SP

Sana Yasin , Syed Asad Hussain , Sinem Aslan , Imran Raza , Muhammad Muzammel , Alice Othmani This is my paper · ORCID
classification q-bio.NCcs.LGeess.SP
keywords Electroencephalogram(EEG)MajorDepressiveDisorder(MDD)Bipolar(BD)ArtificialneuralnetworksbiomedicalinformaticsdeeplearningEEGbiomarkerssystematicreview
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Neural networks fed by electroencephalogram (EEG) signals could make the diagnosis of major depressive disorder (MDD) and bipolar disorder (BD) more objective, and this paper sets out to map that field systematically. It claims to be the first comprehensive survey covering both shallow architectures (multilayer perceptrons, feed-forward and probabilistic networks) and deep architectures (CNNs, LSTMs, deep belief networks) for both disorders. Across the studies it tabulates, deep learning models generally reach higher classification accuracy than shallow models, with the highest MDD figures around 99% and BD figures in the 91-98% range. The review also catalogs EEG recording protocols, candidate biomarkers, and publicly available datasets, and concludes that EEG-based neural networks are useful but not yet clinically trustworthy because they lack interpretability and shared benchmark data.

What carries the argument

The argument is carried by a two-way taxonomy. On the model side, shallow networks (multilayer perceptrons, feed-forward and back-propagation networks, probabilistic neural networks, neuro-fuzzy hybrids) are separated from deep networks (CNNs, LSTMs, RNNs, denoising autoencoders, deep belief networks, CNN-LSTM hybrids). On the data side, the review tracks the elements of EEG experimental protocols that determine what any model learns: participant selection instruments, electrode count, wet versus dry electrodes, 10-20 or 10-10 placement, target brain lobes, and artifact filtering. That structure is what lets the paper compare reported accuracies across otherwise incompatible studies and attribute the high end of the range to deep, feature-learning architectures.

What would settle it

Run the keyword string printed in Section 2 on the six listed databases exactly as given: the empty term ('OR ""') makes the query malformed, so no reproducible result set exists. A corrected query, with that empty term removed, would yield a concrete list of articles; the paper's 'first comprehensive survey' claim would fail if any retrieved neural-network EEG study of MDD or BD is missing from its tables, or if a retrieved set shows that a prior survey already covered the same scope.

Watch

Extended reading notes

Core claim

On its own terms, the paper's central discovery is that EEG-based neural networks form a working, rapidly growing route to automated MDD and BD screening, and that within that route deep architectures outperform shallow ones in most reported comparisons. For MDD, this claim is carried by studies reporting 95-99% accuracy for CNNs, CNN-LSTM hybrids, and related deep models, often using frontal electrodes with the 10-20 placement standard. For BD, the paper finds only shallow neural-network studies, with best accuracies around 91-98%, and no public EEG dataset for bipolar disorder. The paper also asserts that questionnaire-based assessment remains subjective and that EEG biomarkers, such as alpha asymmetry, theta activity, and gamma bursts, offer a more objective alternative, but that the black-box character of neural classifiers is the main obstacle to clinical use.

Load-bearing premise

The load-bearing premise is that the literature search actually retrieved the relevant studies; as printed, the keyword list contains an empty search term that makes the query invalid, so the exhaustiveness that would justify a 'first comprehensive survey' claim is not demonstrated.

Editorial extensions

If this is right

  • If the accuracy levels reported in the surveyed studies hold, EEG plus neural networks could provide a low-cost, non-invasive screening layer for MDD and BD that does not depend on a patient's self-report.
  • Deep models, especially CNNs and CNN-LSTM hybrids, would be the natural starting architecture for new MDD detection systems, since they occupy the high-accuracy end of the paper's tables.
  • Bipolar disorder research will remain difficult to compare across groups until a public EEG dataset exists; the paper finds that none does.
  • Frontal-lobe electrode placements, used in the majority of high-accuracy studies, provide a concrete sensor-design guideline for future work.
  • Clinical adoption of any of these models depends on interpretability, since clinicians need a defensible reason for a predicted diagnosis.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editors' inference: If the deep-over-shallow performance gap is real, hand-crafted feature engineering may become optional for EEG-based MDD screening, because deep networks can learn spatial and temporal patterns directly from raw signals.
  • Editors' inference: The absence of public EEG bipolar datasets means the 91-98% BD accuracies are single-site, small-sample results; the binding constraint on progress is likely reproducibility, not model choice.
  • Editors' inference: A direct, testable extension of the review would be to run one fixed CNN architecture on the HBN, EMBARC, and Depresjon datasets under identical preprocessing and compare cross-dataset performance; the paper catalogs these datasets but does not benchmark them together.
  • Editors' inference: Since more than 90% of the reviewed studies record from the frontal lobe, a cheap empirical question is whether a low-density frontal montage alone can preserve classification accuracy; if it can, screening devices become simpler and cheaper.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 5 minor

Summary. This manuscript presents a systematic review of neural-network-based approaches (shallow and deep) for detecting Major Depressive Disorder (MDD) and Bipolar Disorder (BD) from EEG signals. It describes the search strategy and eligibility criteria, summarizes clinical background and EEG protocols, lists public datasets, and tabulates classification accuracies reported in primary studies. The paper claims to be the first comprehensive survey of both shallow and deep neural network methods for EEG-based MDD and BD detection, and it concludes that deep neural networks generally offer higher classification accuracy than shallow neural network methods, while also discussing limitations and recommending future directions such as explainable AI and multimodal approaches.

Significance. The topic is clinically and technically important: a reliable synthesis of EEG-based neural-network approaches for MDD and BD could guide researchers and clinicians toward reproducible protocols and realistic performance expectations. The manuscript compiles a large number of primary studies and presents structured tables of participants, EEG devices, electrode placements, and reported accuracies, which is useful groundwork. However, as submitted, the review's central methodological claims are not supported: the search string is invalid and unreproducible, the eligibility criteria are incoherent, many references are duplicated, and the numerical evidence underlying the main comparative conclusion is internally inconsistent. These issues undermine the paper's value as a systematic synthesis, though the underlying topic remains significant and the raw material could be reorganized into a useful survey after substantial revision.

major comments (5)
  1. [Section 2 (Search Strategy)] The keyword string contains a literal empty search term, visible as 'OR "" OR' in the list following 'Major depressive disorder', which makes the query syntactically invalid and unreproducible. The claim that the review was conducted exhaustively and in conformity with PRISMA is therefore not supported by the reported search strategy.
  2. [Section 2 (Eligibility Criteria)] The sentence 'Only those subjects are included in this survey that have more than 13 depression severity scores and no prior history of drug and medication' is incoherent as an eligibility criterion: it conflates study-level inclusion with subject-level clinical thresholds (e.g., BDI-II > 13) and is not operationalized. This prevents replication of the review and should be rewritten to distinguish study inclusion criteria from subject-level clinical selection criteria.
  3. [References and Tables 7-12] Multiple references are duplicated, inflating the apparent number of included studies. For example, [75] and [130] are the same article (Li et al., EEG-based mild depression recognition using convolutional neural network, Medical & Biological Engineering & Computing, 2019); additionally, [118] duplicates [113], [121] duplicates [158], [122] duplicates [127], [126] duplicates [131], [119] duplicates [129], [60] duplicates [136], and [139] duplicates [120]. These duplicates appear as distinct rows in Table 12 and elsewhere, so the survey's study counts and any summary statistics derived from them are unreliable.
  4. [Section 5.2.3 and Table 12] The accuracy figures for the same studies disagree between the running text and Table 12. For instance, the text reports [53] at 95.97%, [112] at 93.5%, and [54] at 97.66%, whereas Table 12 lists 98.32%, 95.49%, and 99.12% for these same references. The authors do not acknowledge or reconcile these discrepancies, making the numerical evidence internally inconsistent.
  5. [Section 9 and Table 12] The central conclusion that 'Deep neural networks offer high classification accuracy ... in comparison to shallow neural networks based methods' is not supported by the paper's own Table 12. Shallow classifiers achieve 99.5% ([124]) and 98.75% ([116]), while deep models such as [128] (79.08%), [155] (80.74%), [125] (80%), and [73] (77.20%) score much lower. No comparison is matched on dataset, task, or evaluation protocol, so even internally consistent accuracies would not justify a general superiority claim. The conclusion should be substantially weakened or replaced by a statement that reported accuracies vary widely and that no direct comparison is possible from the surveyed evidence.
minor comments (5)
  1. [Section 3.3] The text refers to 'Table 9 of Section 3', but Table 9 appears in Section 5; the cross-reference should be corrected.
  2. [Figure 1 caption] The caption lists the panels as 'a) ... b) ... b) ...', with the label 'b' used twice; the third panel should be labeled 'c'.
  3. [Tables 7 and 8] Reference [117] appears multiple times in Tables 7 and 8 with different participant counts and selection criteria, indicating that the tables have not been cleaned of duplicate entries; this should be resolved.
  4. [Section 5.1] The statement that 'only ten out of fifty studies include thirty participants' is not verifiable from Table 7, which lists more than fifty rows and contains duplicate references; the count should be recomputed after deduplication.
  5. [Section 7.1.1] The claim that 'no EEG based public datasets are available for bipolar disorder recognition research' is technically correct only if 'EEG-based' is strictly interpreted; the Depresjon dataset described in Section 5.1.1 includes unipolar and bipolar patients but is motor-activity data. The distinction should be stated explicitly to avoid confusion.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular reasoning: the paper is a literature review that compiles external study results and does not derive any prediction from its own inputs.

full rationale

The manuscript is a narrative/structured review rather than an empirical or theoretical derivation, so the circularity patterns enumerated in the rubric do not apply. It summarizes clinical background, protocols, datasets, and reported accuracies from primary studies; none of its claims are obtained by fitting a parameter to a subset of data and then predicting that same subset, and no quantity is defined in terms of another quantity within the review itself. The main comparative claim about deep versus shallow networks is an interpretive summary of the surveyed literature, and while the supporting accuracy numbers are internally inconsistent (for example, the text reports [53] as 95.97% while Table 12 lists 98.32%), inconsistency is a correctness or reporting flaw, not circularity. The paper does contain self-citations to later work by the authors (e.g., [111], [152], [185] involve Othmani or Muzammel), but these are not used to justify the review's central premise, to forbid alternatives, or to import an unverified uniqueness result; they are ordinary references to the authors' own related speech-based depression work within a broader literature compilation. The novelty claim of being the first comprehensive survey is a historical assertion supported by Table 1's comparison of prior surveys, not by a self-referential theorem, and any defect in that claim would be an accuracy issue rather than a circular reduction. The malformed keyword string containing an empty OR term undermines reproducibility of the search but does not make any derived result equivalent to its inputs. Accordingly, no specific circular step can be quoted, and the appropriate score is 0.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The review relies on assumptions about the completeness and accuracy of the literature search and the fidelity of its summaries of primary studies. No free parameters or invented entities are involved.

assumptions (3)
  • domain assumption The literature search and PRISMA protocol were correctly applied.
    The paper claims conformance to PRISMA and uses this to assert the systematic nature of the review. The broken keyword string and incomplete reporting make this assumption unsupported.
  • domain assumption The descriptions of the primary studies are accurate representations of the original papers.
    The review does not re-analyze primary data and must assume that each cited paper reports its methods and results correctly. The presence of duplicated references and internal inconsistencies suggests this assumption is not fully satisfied.
  • domain assumption The keyword search covered all relevant literature.
    Exhaustiveness is claimed as a key part of the review's value, and the search strategy is the only mechanism ensuring coverage. The invalid query string means this assumption is not met.

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Cite this review

Pith. "Pith review of EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks: A review." pith.science (2026). https://pith.science/paper/DGKNJDSB

@misc{pith2026200913402,
  author       = {Pith},
  title        = {Pith review of: EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks: A review},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DGKNJDSB}},
  note         = {Machine review of arXiv:2009.13402}
}
read the original abstract

Mental disorders represent critical public health challenges as they are leading contributors to the global burden of disease and intensely influence social and financial welfare of individuals. The present comprehensive review concentrate on the two mental disorders: Major depressive Disorder (MDD) and Bipolar Disorder (BD) with noteworthy publications during the last ten years. There is a big need nowadays for phenotypic characterization of psychiatric disorders with biomarkers. Electroencephalography (EEG) signals could offer a rich signature for MDD and BD and then they could improve understanding of pathophysiological mechanisms underling these mental disorders. In this review, we focus on the literature works adopting neural networks fed by EEG signals. Among those studies using EEG and neural networks, we have discussed a variety of EEG based protocols, biomarkers and public datasets for depression and bipolar disorder detection. We conclude with a discussion and valuable recommendations that will help to improve the reliability of developed models and for more accurate and more deterministic computational intelligence based systems in psychiatry. This review will prove to be a structured and valuable initial point for the researchers working on depression and bipolar disorders recognition by using EEG signals.

Figures

Figures reproduced from arXiv: 2009.13402 by the authors.

Figure 1
Figure 1. a)Brain structure and EEG waves representation: b) Different brain lobes and their location; b) The placement of EEG electrodes at four brain lobes using 10-20 international system. According to the World Health Organization (WHO)4 , de￾pression is a common psychiatric disorder characterized by a persistent undesirable effect like sadness, lack of atten￾tion, or pleasure in formerly satisfying or enjoyable activitie… view at source ↗
Figure 2
Figure 2. Ratio of Deep Learning studies adopting EEG signals for a variety of applications referred to [154]. The neucube architecture is compared with Multi-layer Per￾ceptron (MLP) and other traditional machine learning meth￾ods. The results shows that neucube performs better than MLP and all other traditional machine learning algorithms such as SVM, decision tree and logistic regression. Deep learning-based depression dete… view at source ↗

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Reference graph

Works this paper leans on

176 extracted references · 80 canonical work pages

  1. [130]

    X.Li,R.La,Y.Wang,J.Niu,S.Zeng,S.Sun,J.Zhu,Eeg-basedmild depression recognition using convolutional neural network, Medical & biological engineering & computing 57 (6) (2019) 1341–1352

    2019

  2. [116]

    S.D.Puthankattil,P.K.Joseph,Half-wavesegmentfeatureextraction ofeegsignalsofpatientswithdepressionandperformanceevaluation of neural network classifiers, Journal of Mechanics in Medicine and Biology 17 (01) (2017) 1750006

    work page 2017

  3. [124]

    Faust, P

    O. Faust, P. C. A. Ang, S. D. Puthankattil, P. K. Joseph, Depression diagnosis support system based on eeg signal entropies, Journal of mechanics in medicine and biology 14 (03) (2014) 1450035

    work page 2014

  4. [73]

    W. Mao, J. Zhu, X. Li, X. Zhang, S. Sun, Resting state eeg based depression recognition research using deep learning method, in: In- ternational Conference on Brain Informatics, Springer, 2018, pp. 329–338

    2018

  5. [128]

    Z.Wan,J.Huang,H.Zhang,H.Zhou,J.Yang,N.Zhong,Hybrideeg- net: A convolutional neural network for eeg feature learning and depression discrimination, IEEE Access 8 (2020) 30332–30342

    work page 2020

  6. [155]

    X.Li,R.La,Y.Wang,B.Hu,X.Zhang,Adeeplearningapproachfor mild depression recognition based on functional connectivity using electroencephalography, Frontiers in Neuroscience 14 (2020)

    work page 2020

  7. [125]

    S. D. Kumar, D. Subha, Prediction of depression from eeg signal using long short term memory (lstm), in: 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), IEEE, 2019, pp. 1248–1253

    work page 2019

  8. [53]

    Mumtaz, A

    W. Mumtaz, A. Qayyum, A deep learning framework for automatic diagnosis of unipolar depression, International journal of medical informatics 132 (2019) 103983

    2019

  9. [112]

    U. R. Acharya, S. L. Oh, Y. Hagiwara, J. H. Tan, H. Adeli, D. P. Subha, Automated eeg-based screening of depression using deep convolutional neural network, Computer methods and programs in biomedicine 161 (2018) 103–113

    work page 2018

  10. [54]

    B.Ay,O.Yildirim,M.Talo,U.B.Baloglu,G.Aydin,S.D.Puthankat- til, U. R. Acharya, Automated depression detection using deep repre- sentation and sequence learning with eeg signals, Journal of medical systems 43 (7) (2019) 205

    2019

  11. [118]

    1339–1344

    P.Sandheep,S.Vineeth,M.Poulose,D.Subha,Performanceanalysis of deep learning cnn in classification of depression eeg signals, in: TENCON2019-2019IEEERegion10Conference(TENCON),IEEE, 2019, pp. 1339–1344

    work page 2019

  12. [158]

    D. Shah, G. Y. Wang, M. Doborjeh, Z. Doborjeh, N. Kasabov, Deep learning of eeg data in the neucube brain-inspired spiking neural network architecture for a better understanding of depression, in: InternationalConferenceonNeuralInformationProcessing,Springer, 2019, pp. 195–206

    work page 2019

  13. [127]

    X.Zhang,B.Hu,L.Zhou,P.Moore,J.Chen,Aneegbasedpervasive depression detection for females, in: Joint International Conference on Pervasive Computing and the Networked World, Springer, 2012, pp. 848–861

    work page 2012

  14. [126]

    Mahato, S

    S. Mahato, S. Paul, Detection of major depressive disorder using linear and non-linear features from eeg signals, Microsystem Tech- nologies 25 (3) (2019) 1065–1076

    work page 2019

  15. [131]

    Mahato, S

    S. Mahato, S. Paul, Detection of major depressive disorder using linear and non-linear features from eeg signals, Microsystem Tech- nologies 25 (3) (2019) 1065–1076. Sana Yasin et al.:Preprint submitted to Elsevier Page 27 of 29 EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks: A review

    work page 2019

  16. [129]

    Mallikarjun, H

    H. Mallikarjun, H. Suresh, Depression level prediction using eeg sig- nal processing, in: 2014 International Conference on Contemporary Computing and Informatics (IC3I), IEEE, 2014, pp. 928–933

    work page 2014

  17. [136]

    Mohan, S

    Y. Mohan, S. S. Chee, D. K. P. Xin, L. P. Foong, Artificial neural network for classification of depressive and normal in eeg, in: 2016 IEEE EMBS conference on biomedical engineering and sciences (IECBES), IEEE, 2016, pp. 286–290

    2016

  18. [139]

    J. Zhu, Y. Wang, R. La, J. Zhan, J. Niu, S. Zeng, X. Hu, Multi- modal mild depression recognition based on eeg-em synchronization acquisition network, IEEE Access 7 (2019) 28196–28210

    work page 2019

Show all 176 references
  1. [1]

    W. Chow, M. Doane, J. Sheehan, L. Alphs, H. Le, Le h. economic burden among patients with major depressive disorder: an analysis of healthcare resource use, work productivity, and direct and indirect costsbydepressionseverity,AmJManagCare16(2019)e188–e196

    2019

  2. [2]

    Sartorius, Depression and diabetes, Dialogues in clinical neuro- science 20 (1) (2018) 47

    N. Sartorius, Depression and diabetes, Dialogues in clinical neuro- science 20 (1) (2018) 47

    2018

  3. [3]

    T. T. T. Tran, N. B. Nguyen, M. A. Luong, T. H. A. Bui, T. D. Phan, T. H. Ngo, H. Minas, T. Q. Nguyen, et al., Stress, anxiety and depression in clinical nurses in vietnam: a cross-sectional survey and cluster analysis, International journal of mental health systems 13 (1) (2019) 3

    2019

  4. [4]

    K.-M. Han, D. De Berardis, M. Fornaro, Y.-K. Kim, Differentiating between bipolar and unipolar depression in functional and structural mri studies, Progress in Neuro-Psychopharmacology and Biological Psychiatry 91 (2019) 20–27

    2019

  5. [7]

    Zafar, S

    A. Zafar, S. Chitnis, Survey of depression detection using social networking sites via data mining, in: 2020 10th International Confer- enceonCloudComputing,DataScience&Engineering(Confluence), IEEE, 2020, pp. 88–93

    2020

  6. [9]

    De Berardis, G

    F.Vellante, F.Ferri, G.Baroni, P.Croce, D.Migliorati, M.Pettoruso, D. De Berardis, G. Martinotti, F. Zappasodi, M. Di Giannantonio, Euthymic bipolar disorder patients and eeg microstates: a neural signature of their abnormal self experience?, Journal of Affective Disorders (2020)

    2020

  7. [10]

    Y.Wang,B.McCane,N.McNaughton,Z.Huang,P.Neo,etal.,Anxi- etydecoder: Aneeg-basedanxietypredictorusinga3-dconvolutional neural network, in: 2019 International Joint Conference on Neural Networks (IJCNN), IEEE, 2019, pp. 1–8

    2019

  8. [11]

    Maziade, The electroretinogram may differentiate schizophrenia from bipolar disorder, Biological psychiatry 87 (3) (2020) 263–270

    M.Hébert,C.Mérette,A.-M.Gagné,T.Paccalet,I.Moreau,J.Lavoie, M. Maziade, The electroretinogram may differentiate schizophrenia from bipolar disorder, Biological psychiatry 87 (3) (2020) 263–270. Sana Yasin et al.:Preprint submitted to Elsevier Page 24 of 29 EEG based Major Depre...

    2020

  9. [12]

    S. I. Dimitriadis, C. I. Salis, D. Liparas, A sleep disorder detection model based on eeg cross-frequency coupling and random forest, medRxiv (2020)

    2020

  10. [13]

    Mahato, S

    S. Mahato, S. Paul, Electroencephalogram (eeg) signal analysis for diagnosis of major depressive disorder (mdd): a review, in: Nano- electronics, Circuits and Communication Systems, Springer, 2019, pp. 323–335

    2019

  11. [14]

    Phadikar, N

    S. Phadikar, N. Sinha, R. Ghosh, Automatic eye blink artifact re- moval from eeg signal using wavelet transform with heuristically optimized threshold, IEEE Journal of Biomedical and Health Infor- matics (2020)

    2020

  12. [15]

    Grunze, Bipolar disorder, in: Neurobiology of brain disorders, Elsevier, 2015, pp

    H. Grunze, Bipolar disorder, in: Neurobiology of brain disorders, Elsevier, 2015, pp. 655–673

    2015

  13. [16]

    M. L. Phillips, D. J. Kupfer, Bipolar disorder diagnosis: challenges and future directions, The Lancet 381 (9878) (2013) 1663–1671

    2013

  14. [17]

    /uni010C

    M. /uni010C. Radenkovi/uni0107, V. L. Lopez, Machine learning approaches for detecting the depression from resting-state electroencephalogram (eeg): A review study, arXiv preprint arXiv:1909.03115 (2019)

    2019 arXiv

  15. [18]

    Malviya, R

    A. Malviya, R. Meharkure, R. Narsinghani, V. Sheth, P. Meshram, Depressiondetectionthroughspeechanalysis: Asurvey,International JournalofScientificResearchinComputerScience, Engineeringand Information Technology (2019) 712–716

    2019

  16. [19]

    J.Oh,K.Yun,U.Maoz,T.-S.Kim,J.-H.Chae,Identifyingdepression in the national health and nutrition examination survey data using a deep learning algorithm, Journal of affective disorders 257 (2019) 623–631

    2019

  17. [20]

    Kerst, J

    A. Kerst, J. Zielasek, W. Gaebel, Smartphone applications for de- pression: a systematic literature review and a survey of health care professionals’ attitudes towards their use in clinical practice, Euro- pean archives of psychiatry and clinical neuroscience 270 (2) (2020) 139–152

    2020

  18. [21]

    Zhang, L

    X. Zhang, L. Yao, X. Wang, J. Monaghan, D. Mcalpine, Y. Zhang, A survey on deep learning based brain computer interface: Recent advances and new frontiers, arXiv preprint arXiv:1905.04149 (2019)

    2019 arXiv

  19. [22]

    Iftikhar, S

    M. Iftikhar, S. A. Khan, A. Hassan, A survey of deep learning and traditional approaches for eeg signal processing and classification, in: 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), IEEE, 2018, pp. 395–400

    2018

  20. [23]

    Bozhkov, P

    L. Bozhkov, P. Georgieva, Overview of deep learning architectures for eeg-based brain imaging, in: 2018 International Joint Conference on Neural Networks (IJCNN), IEEE, 2018, pp. 1–7

    2018

  21. [24]

    A.S.Fathima,S.Mythili,Deeplearningtechniqueforfeatureclassifi- cationofeegtoaccessstudent’smentalstatus: Asurvey,International Research Journal of Engineering and Technology (IRJET) (2019)

    2019

  22. [25]

    A.Saikia,S.Paul,Applicationofdeeplearningforeeg,in: Handbook ofResearchonAdvancementsofArtificialIntelligenceinHealthcare Engineering, IGI Global, 2020, pp. 106–123

    2020

  23. [26]

    W. Liu, Z. Wang, X. Liu, N. Zeng, Y. Liu, F. E. Alsaadi, A survey of deep neural network architectures and their applications, Neurocom- puting 234 (2017) 11–26

    2017

  24. [27]

    Pouyanfar, S

    S. Pouyanfar, S. Sadiq, Y. Yan, H. Tian, Y. Tao, M. P. Reyes, M.- L. Shyu, S.-C. Chen, S. Iyengar, A survey on deep learning: Al- gorithms, techniques, and applications, ACM Computing Surveys (CSUR) 51 (5) (2018) 1–36

    2018

  25. [28]

    J.Pamina,B.Raja,Surveyondeeplearningalgorithms,International Journal of Emerging Technology and Innovative Engineering 5 (1) (2019)

    2019

  26. [29]

    C. Tan, F. Sun, T. Kong, W. Zhang, C. Yang, C. Liu, A survey on deeptransferlearning,in: Internationalconferenceonartificialneural networks, Springer, 2018, pp. 270–279

    2018

  27. [30]

    M. Z. Alom, T. M. Taha, C. Yakopcic, S. Westberg, P. Sidike, M. S. Nasrin, M. Hasan, B. C. Van Essen, A. A. Awwal, V. K. Asari, A state-of-the-art survey on deep learning theory and architectures, Electronics 8 (3) (2019) 292

    2019

  28. [31]

    H.-J.Hwang,S.Kim,S.Choi,C.-H.Im,Eeg-basedbrain-computerin- terfaces: athoroughliteraturesurvey,InternationalJournalofHuman- Computer Interaction 29 (12) (2013) 814–826

    2013

  29. [32]

    Rashid, N

    M. Rashid, N. Sulaiman, M. Mustafa, S. Khatun, B. S. Bari, M. J. Hasan, Recent trends and open challenges in eeg based brain- computer interface systems, in: InECCE2019, Springer, 2020, pp. 367–378

    2020

  30. [33]

    Khosla, P

    A. Khosla, P. Khandnor, T. Chand, A comparative analysis of signal processingandclassificationmethodsfordifferentapplicationsbased on eeg signals, Biocybernetics and Biomedical Engineering (2020)

    2020

  31. [34]

    Bhattacharjee, S

    S. Bhattacharjee, S. Ghatak, S. Dutta, B. Chatterjee, M. Gupta, A survey on comparison analysis between eeg signal and mri for brain stroke detection, in: Emerging Technologies in Data Mining and Information Security, Springer, 2019, pp. 377–382

    2019

  32. [35]

    X. Gu, Z. Cao, A. Jolfaei, P. Xu, D. Wu, T.-P. Jung, C.-T. Lin, Eeg- based brain-computer interfaces (bcis): A survey of recent studies on signal sensing technologies and computational intelligence ap- proaches and their applications, arXiv preprint arXiv:2001.11337 (2020)

    2020 arXiv

  33. [36]

    Alhassan, A

    A. Alhassan, A. R. Ziblim, S. Muntaka, A survey on depression amonginfertilewomeninghana, BMCwomen’shealth14(1)(2014) 1–6

    2014

  34. [37]

    Koyama, T

    F. Koyama, T. Yoda, T. Hirao, Insomnia and depression: Japanese hospital workers questionnaire survey, Open Medicine 12 (1) (2017) 391–398

    2017

  35. [38]

    Heiden-Rootes, A

    K. Heiden-Rootes, A. Wiegand, D. Thomas, R. M. Moore, K. A. Ross, A national survey on depression, internalized homophobia, college religiosity, and climate of acceptance on college campuses for sexual minority adults, Journal of Homosexuality 67 (4) (2020) 435–451

    2020

  36. [39]

    J. N. Miller, D. W. Black, Bipolar disorder and suicide: A review, Current psychiatry reports 22 (2) (2020) 6

    2020

  37. [40]

    Bai, M.-H

    Y.-M. Bai, M.-H. Chen, J.-W. Hsu, K.-L. Huang, P.-C. Tu, W.-C. Chang,T.-P.Su,C.T.Li,W.-C.Lin,S.-J.Tsai,Acomparisonstudyof metabolicprofiles,immunity,andbraingraymattervolumesbetween patients with bipolar disorder and depressive disorder, Journal of Neuroinflammation 17 (1) (2020) 42

    2020

  38. [41]

    Degabriele, J

    R. Degabriele, J. Lagopoulos, A review of eeg and erp studies in bipolar disorder, Acta Neuropsychiatrica 21 (2) (2009) 58–66

    2009

  39. [42]

    G.Zhang,W.Wang,Z.Yang,S.Zhan,F.Sun,Introductiontoprisma- ci extension statement and checklist systematic reviews on complex interventions, Zhonghua liu xing bing xue za zhi= Zhonghua liux- ingbingxue zazhi 40 (7) (2019) 832–838

    2019

  40. [43]

    M.Staples,M.Niazi,Experiencesusingsystematicreviewguidelines, Journal of Systems and Software 80 (9) (2007) 1425–1437

    2007

  41. [44]

    Strech, N

    D. Strech, N. Sofaer, How to write a systematic review of reasons, Journal of Medical Ethics 38 (2) (2012) 121–126

    2012

  42. [45]

    D.P.Subha,P.K.Joseph,R.Acharya,C.M.Lim,Eegsignalanalysis: a survey, Journal of medical systems 34 (2) (2010) 195–212

    2010

  43. [46]

    Siuly, Y

    S. Siuly, Y. Li, Y. Zhang, Electroencephalogram (eeg) and its back- ground, in: EEG Signal Analysis and Classification, Springer, 2016, pp. 3–21

    2016

  44. [47]

    Goudiaby, A

    B. Goudiaby, A. Othmani, A. Nait-Ali, Eeg biometrics for person verification, in: Hidden Biometrics, Springer, 2020, pp. 45–69

    2020

  45. [48]

    X. Ma, H. Yang, Q. Chen, D. Huang, Y. Wang, Depaudionet: An efficientdeepmodelforaudiobaseddepressionclassification,in: Pro- ceedings of the 6th international workshop on audio/visual emotion challenge, 2016, pp. 35–42

    2016

  46. [49]

    M.Nasir,A.Jati,P.G.Shivakumar,S.NallanChakravarthula,P.Geor- giou, Multimodal and multiresolution depression detection from speech and facial landmark features, in: Proceedings of the 6th in- ternational workshop on audio/visual emotion challenge, 2016, pp. 43–50

    2016

  47. [50]

    B. Sun, Y. Zhang, J. He, L. Yu, Q. Xu, D. Li, Z. Wang, A random forest regression method with selected-text feature for depression assessment, in: Proceedings of the 7th Annual Workshop on Au- dio/Visual Emotion Challenge, 2017, pp. 61–68

    2017

  48. [51]

    Sharma, T

    N. Sharma, T. Gedeon, Objective measures, sensors and computa- tional techniques for stress recognition and classification: A survey, Computer methods and programs in biomedicine 108 (3) (2012) 1287–1301. Sana Yasin et al.:Preprint submitted to Elsevier Page 25 of 29 EEG based ...

    2012

  49. [52]

    C. S. Nayak, A. C. Anilkumar, Eeg normal waveforms, in: StatPearls [Internet], StatPearls Publishing, 2019

    2019

  50. [55]

    X. Li, X. Zhang, J. Zhu, W. Mao, S. Sun, Z. Wang, C. Xia, B. Hu, Depression recognition using machine learning methods with differ- ent feature generation strategies, Artificial intelligence in medicine 99 (2019) 101696

    2019

  51. [56]

    Mohammadzadeh, M

    B. Mohammadzadeh, M. Khodabandelu, M. Lotfizadeh, Comparing diagnosis of depression in depressed patients by eeg, based on two algorithms: Artificialnervenetworksandneuro-fuzynetworks, Inter- national Journal of Epidemiologic Research 3 (3) (2016) 246–258

    2016

  52. [57]

    T.T.Erguzel,G.H.Sayar,N.Tarhan,Artificialintelligenceapproach to classify unipolar and bipolar depressive disorders, Neural Com- puting and Applications 27 (6) (2016) 1607–1616

    2016

  53. [58]

    S.M.Alarcao,M.J.Fonseca,Emotionsrecognitionusingeegsignals: A survey, IEEE Transactions on Affective Computing 10 (3) (2017) 374–393

    2017

  54. [59]

    T.Alotaiby,F.E.AbdEl-Samie,S.A.Alshebeili,I.Ahmad,Areview of channel selection algorithms for eeg signal processing, EURASIP Journal on Advances in Signal Processing 2015 (1) (2015) 66

    2015

  55. [61]

    URL https://www.healthline.com/health/depression/ effects-brain#1

    T.J.L.EricaCirino,Theeffectsofdepressiononthebrain(February 8, 2017). URL https://www.healthline.com/health/depression/ effects-brain#1

    2017

  56. [62]

    G. E. Simon, M. VonKorff, M. Piccinelli, C. Fullerton, J. Ormel, An international study of the relation between somatic symptoms and depression, New England journal of medicine 341 (18) (1999) 1329–1335

    1999

  57. [63]

    T. M. H., The link between depression and physical symptoms, Pri- mary care companion to the Journal of clinical psychiatry (2004) 12–16

    2004

  58. [64]

    G. G. M, Depression and associated physical diseases and symptoms, Dialogues in clinical neuroscience 8(2) (2006) 259–265

    2006

  59. [65]

    M.Guha,Diagnosticandstatisticalmanualofmentaldisorders: Dsm- 5, Reference Reviews (2014)

    2014

  60. [66]

    Pampouchidou, P

    A. Pampouchidou, P. Simos, K. Marias, F. Meriaudeau, F. Yang, M. Pediaditis, M. Tsiknakis, Automatic assessment of depression based on visual cues: A systematic review, IEEE Transactions on Affective Computing (2017)

    2017

  61. [67]

    N. R. Council, et al., Depression in parents, parenting, and children: Opportunities to improve identification, treatment, and prevention, National Academies Press, 2009

    2009

  62. [68]

    Rapkin, A review of treatment of premenstrual syndrome & pre- menstrual dysphoric disorder, Psychoneuroendocrinology 28 (2003) 39–53

    A. Rapkin, A review of treatment of premenstrual syndrome & pre- menstrual dysphoric disorder, Psychoneuroendocrinology 28 (2003) 39–53

    2003

  63. [69]

    Tondo, P

    G.Serra,A.Koukopoulos,L.DeChiara,A.E.Koukopoulos,G.Sani, L. Tondo, P. Girardi, D. Reginaldi, R. J. Baldessarini, Early clinical predictorsoflong-termmorbidityinmajordepressivedisorder, Early intervention in psychiatry 13 (4) (2019) 999–1002

    2019

  64. [70]

    A. P. Association, A. P. Association, et al., Diagnostic and statistical manual of mental disorders: Dsm-5 (2013)

    2013

  65. [71]

    Kroenke, R

    K. Kroenke, R. L. Spitzer, J. B. Williams, The phq-9: validity of a brief depression severity measure, Journal of general internal medicine 16 (9) (2001) 606–613

    2001

  66. [72]

    H. Cai, J. Han, Y. Chen, X. Sha, Z. Wang, B. Hu, J. Yang, L. Feng, Z.Ding,Y.Chen,etal.,Apervasiveapproachtoeeg-baseddepression detection, Complexity 2018 (2018)

    2018

  67. [74]

    A. T. Beck, R. A. Steer, G. K. Brown, et al., Beck depression inventory-ii, San Antonio 78 (2) (1996) 490–498

    1996

  68. [76]

    A. P. Association, et al., American psychiatric association: Diag- nostic and statistical manual of mental disorders, (p. 81), Arlington: American Psychiatric Association (2013)

    2013

  69. [77]

    T. T. Erguzel, S. Ozekes, S. Gultekin, N. Tarhan, G. H. Sayar, A. Bayram, Neural network based response prediction of rtms in major depressive disorder using qeeg cordance, Psychiatry investiga- tion 12 (1) (2015) 61

    2015

  70. [78]

    Ahmadlou, H

    M. Ahmadlou, H. Adeli, A. Adeli, Fractality analysis of frontal brain in major depressive disorder, International Journal of Psychophysiol- ogy 85 (2) (2012) 206–211

    2012

  71. [79]

    L.S.Radloff,Aself-reportdepressionscaleforresearchinthegeneral population, Applied psychol Measurements 1 (1977) 385–401

    1977

  72. [80]

    R. M. Saracino, H. Cham, B. Rosenfeld, C. J. Nelson, Confirmatory factor analysis of the center for epidemiologic studies depression scale in oncology with examination of invariance between younger and older patients., European Journal of Psychological Assessment (2018)

    2018

  73. [81]

    Timmerby, J

    N. Timmerby, J. H. Andersen, S. Søndergaard, S. D. Østergaard, P. Bech, A systematic review of the clinimetric properties of the 6-item version of the hamilton depression rating scale (ham-d6), Psychotherapy and psychosomatics 86 (3) (2017) 141–149

    2017

  74. [82]

    B.D.Nelson,E.M.Kessel,D.N.Klein,S.A.Shankman,Depression symptomdimensionsandasymmetricalfrontalcorticalactivitywhile anticipating reward, Psychophysiology 55 (1) (2018) e12892

    2018

  75. [83]

    Nusslock, K

    R. Nusslock, K. Walden, E. Harmon-Jones, Asymmetrical frontal corticalactivityassociatedwithdifferentialriskformoodandanxiety disorder symptoms: An rdoc perspective, International Journal of Psychophysiology 98 (2) (2015) 249–261

    2015

  76. [84]

    S.M.Eack,C.G.Greeno,B.-J.Lee,Limitationsofthepatienthealth questionnaire in identifying anxiety and depression in community mental health: many cases are undetected, Research on social work practice 16 (6) (2006) 625–631

    2006

  77. [85]

    Kroenke, R

    K. Kroenke, R. L. Spitzer, The phq-9: a new depression diagnostic and severity measure, Psychiatric annals 32 (9) (2002) 509–515

    2002

  78. [86]

    Hagiwara, Y

    N. Hagiwara, Y. Omiya, S. Shinohara, M. Nakamura, M. Higuchi, S. Mitsuyoshi, H. Yasunaga, S. Tokuno, Validity of mind monitoring system as a mental health indicator using voice, Adv. Sci. Technol. Eng. Syst. J 2 (3) (2017) 338–344

    2017

  79. [87]

    B. D. W. Group, A. J. Atkinson Jr, W. A. Colburn, V. G. DeGruttola, D. L. DeMets, G. J. Downing, D. F. Hoth, J. A. Oates, C. C. Peck, R. T. Schooley, et al., Biomarkers and surrogate endpoints: preferred definitions and conceptual framework, Clinical pharmacology & therapeutics...

    2001

  80. [88]

    B. W. Dunlop, H. S. Mayberg, Neuroimaging-based biomarkers for treatmentselectioninmajordepressivedisorder,Dialoguesinclinical neuroscience 16 (4) (2014) 479

    2014

  81. [89]

    Shadrina, E

    M. Shadrina, E. A. Bondarenko, P. A. Slominsky, Genetics factors in major depression disease, Frontiers in psychiatry 9 (2018) 334

    2018

  82. [90]

    T. J. L. Stephanie Faris, Is depression genetic? (July 25, 2017). URL https://www.healthline.com/health/depression/genetic

    2017

  83. [91]

    W.Mumtaz, A.S.Malik, M.A.M.Yasin, L.Xia, Reviewoneegand erp predictive biomarkers for major depressive disorder, Biomedical Signal Processing and Control 22 (2015) 85–98

    2015

  84. [92]

    S. M. Tripathi, N. Mishra, R. K. Tripathi, K. Gurnani, P300 latency as an indicator of severity in major depressive disorder, Industrial psychiatry journal 24 (2) (2015) 163

    2015

  85. [93]

    Wyss, The ldaep as a potential biomarker for central serotonergic activity: challenges to overcome, Ph.D

    C. Wyss, The ldaep as a potential biomarker for central serotonergic activity: challenges to overcome, Ph.D. thesis, University of Zurich (2015)

    2015

  86. [94]

    Steiger, M

    A. Steiger, M. Kimura, Wake and sleep eeg provide biomarkers in Sana Yasin et al.:Preprint submitted to Elsevier Page 26 of 29 EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks: A review depression, Journal of psychiatric research 44 (4) ...

    2010

  87. [95]

    P. L. Franzen, D. J. Buysse, Sleep disturbances and depression: risk relationships for subsequent depression and therapeutic implications, Dialogues in clinical neuroscience 10 (4) (2008) 473

    2008

  88. [96]

    Van Der Vinne, M

    N. Van Der Vinne, M. A. Vollebregt, M. J. Van Putten, M. Arns, Frontal alpha asymmetry as a diagnostic marker in depression: Fact or fiction? a meta-analysis, Neuroimage: clinical 16 (2017) 79–87

    2017

  89. [97]

    A.Dharmadhikari,A.Tandle,S.Jaiswal,V.Sawant,V.Vahia,N.Jog, et al., Frontal theta asymmetry as a biomarker of depression, East Asian Archives of Psychiatry 28 (1) (2018) 17

    2018

  90. [98]

    A. M. Hunter, T. X. Nghiem, I. A. Cook, D. E. Krantz, M. J. Minzen- berg, A. F. Leuchter, Change in quantitative eeg theta cordance as a potential predictor of repetitive transcranial magnetic stimulation clinical outcome in major depressive disorder, Clinical EEG and Neurosci...

    2018

  91. [99]

    P.J.Fitzgerald, B.O.Watson, Gammaoscillationsasabiomarkerfor major depression: an emerging topic, Translational psychiatry 8 (1) (2018) 1–7

    2018

  92. [101]

    P. C. Koo, C. Berger, G. Kronenberg, J. Bartz, P. Wybitul, O. Reis, J. Hoeppner, Combined cognitive, psychomotor and electrophysio- logical biomarkers in major depressive disorder, European archives of psychiatry and clinical neuroscience 269 (7) (2019) 823–832

    2019

  93. [102]

    Fernández-Palleiro, T

    P. Fernández-Palleiro, T. Rivera-Baltanás, D. Rodrigues-Amorim, S. Fernández-Gil, M. del Carmen Vallejo-Curto, M. Álvarez-Ariza, M.López,C.Rodriguez-Jamardo,J.LuisBenavente,E.deLasHeras, et al., Brainwaves oscillations as a potential biomarker for major de- pression disorder r...

    2020

  94. [103]

    M. M. Caudill, A. M. Hunter, I. A. Cook, A. F. Leuchter, The an- tidepressant treatment response index as a predictor of reboxetine treatment outcome in major depressive disorder, Clinical EEG and neuroscience 46 (4) (2015) 277–284

    2015

  95. [104]

    DeBattista, G

    C. DeBattista, G. Kinrys, D. Hoffman, C. Goldstein, J. Zajecka, J. Kocsis, M. Teicher, S. Potkin, A. Preda, G. Multani, et al., The use of referenced-eeg (reeg) in assisting medication selection for the treatment of depression, Journal of psychiatric research 45 (1) (2011) 64–75

    2011

  96. [105]

    M. Arns, A. Etkin, U. Hegerl, L. M. Williams, C. DeBattista, D. M. Palmer, P. B. Fitzgerald, A. Harris, R. deBeuss, E. Gordon, Frontal and rostral anterior cingulate (racc) theta eeg in depression: Implica- tions for treatment outcome?, European Neuropsychopharmacology 25 (8) ...

    2015

  97. [106]

    A.Wichniak,A.Wierzbicka,W.Jernajczyk,Sleepasabiomarkerfor depression,Internationalreviewofpsychiatry25(5)(2013)632–645

    2013

  98. [107]

    S. Sun, J. Li, H. Chen, T. Gong, X. Li, B. Hu, A study of resting- state eeg biomarkers for depression recognition, arXiv preprint arXiv:2002.11039 (2020)

    2020 arXiv

  99. [108]

    Z. S. Syed, K. Sidorov, D. Marshall, Depression severity prediction based on biomarkers of psychomotor retardation, in: Proceedings of the 7th Annual Workshop on Audio/Visual Emotion Challenge, 2017, pp. 37–43

    2017

  100. [109]

    O. J. Robinson, B. J. Sahakian, Cognitive biomarkers in depression. (2013)

    2013

  101. [110]

    J. R. Williamson, T. F. Quatieri, B. S. Helfer, G. Ciccarelli, D. D. Mehta, Vocal and facial biomarkers of depression based on motor incoordination and timing, in: Proceedings of the 4th International Workshop on Audio/Visual Emotion Challenge, 2014, pp. 65–72

    2014

  102. [111]

    Othmani, D

    A. Othmani, D. Kadoch, K. Bentounes, E. Rejaibi, R. Alfred, A. Ha- did, Towards robust deep neural networks for affect and depression recognition from speech, arXiv preprint arXiv:1911.00310 (2019)

    2019 arXiv

  103. [114]

    Jebelli, M

    H. Jebelli, M. M. Khalili, S. Lee, Mobile eeg-based workers’ stress recognition by applying deep neural network, in: Advances in In- formatics and Computing in Civil and Construction Engineering, Springer, 2019, pp. 173–180

    2019

  104. [115]

    Mohammadi, M

    Y. Mohammadi, M. Hajian, M. H. Moradi, Discrimination of depres- sion levels using machine learning methods on eeg signals, in: 2019 27th Iranian Conference on Electrical Engineering (ICEE), IEEE, 2019, pp. 1765–1769

    2019

  105. [117]

    S. D. Puthankattil, P. K. Joseph, Classification of eeg signals in normal and depression conditions by ann using rwe and signal en- tropy, Journal of Mechanics in Medicine and biology 12 (04) (2012) 1240019

    2012

  106. [123]

    H. Cai, X. Sha, X. Han, S. Wei, B. Hu, Pervasive eeg diagnosis of depression using deep belief network with three-electrodes eeg collector, in: 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, 2016, pp. 1239–1246

    2016

  107. [132]

    H. Cai, Z. Wang, Y. Zhang, Y. Chen, B. Hu, A virtual-reality based neurofeedback game framework for depression rehabilitation using pervasive three-electrode eeg collector, in: Proceedings of the 12th Chinese Conference on Computer Supported Cooperative Work and Social Computi...

    2017

  108. [133]

    Li, F.-y

    Y.-j. Li, F.-y. Fan, Classification of schizophrenia and depression by eeg with anns, in: 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, IEEE, 2006, pp. 2679–2682

    2005

  109. [134]

    H. Peng, B. Hu, Q. Liu, Q. Dong, Q. Zhao, P. Moore, User-centered depression prevention: An eeg approach to pervasive healthcare, in: 2011 5th International Conference on Pervasive Computing Tech- nologies for Healthcare (PervasiveHealth) and Workshops, IEEE, 2011, pp. 325–330

    2011

  110. [135]

    Katyal, S

    Y. Katyal, S. V. Alur, S. Dwivedi, R. Menaka, Eeg signal and video analysis based depression indication, in: 2014 IEEE International Conference on Advanced Communications, Control and Computing Technologies, IEEE, 2014, pp. 1353–1360

    2014

  111. [137]

    H. Kwon, S. Kang, W. Park, J. Park, Y. Lee, Deep learning based pre-screening method for depression with imagery frontal eeg chan- nels, International Conference on Information and Communication Technology Convergence (ICTC) (2019)

    2019

  112. [138]

    Spyrou, C

    I.-M. Spyrou, C. Frantzidis, C. Bratsas, I. Antoniou, P. D. Bamidis, Geriatric depression symptoms coexisting with cognitive decline: a comparison of classification methodologies, Biomedical Signal Processing and Control 25 (2016) 118–129

    2016

  113. [140]

    L. LIU, R. ZHOU, An important neural indicator of measuring de- pression: The asymmetry of resting frontal activity, Advances in Psychological Science 23 (6) (2015) 1000–1008

    2015

  114. [141]

    W. Wu, Y. Zhang, J. Jiang, M. V. Lucas, G. A. Fonzo, C. E. Rolle, C. Cooper, C. Chin-Fatt, N. Krepel, C. A. Cornelssen, et al., An electroencephalographic signature predicts antidepressant response in major depression, Nature biotechnology 38 (4) (2020) 439–447

    2020

  115. [142]

    Garcia-Ceja, M

    E. Garcia-Ceja, M. Riegler, P. Jakobsen, J. Tørresen, T. Nordgreen, K. J. Oedegaard, O. B. Fasmer, Depresjon: a motor activity database of depression episodes in unipolar and bipolar patients, in: Proceed- ings of the 9th ACM Multimedia Systems Conference, 2018, pp. 472–477

    2018

  116. [143]

    Kristjánsdóttir, P

    H. Kristjánsdóttir, P. M. Salkovskis, B. H. Sigurdsson, E. Sigurds- son, A. Agnarsdóttir, J. F. Sigurdsson, Transdiagnostic cognitive behavioural treatment and the impact of co-morbidity: An open trial in a cohort of primary care patients, Nordic journal of psychiatry 70 (3) (...

    2016

  117. [144]

    Langer, E

    N. Langer, E. J. Ho, L. M. Alexander, H. Y. Xu, R. K. Jozanovic, S. Henin, A. Petroni, S. Cohen, E. T. Marcelle, L. C. Parra, et al., A resourceforassessinginformationprocessinginthedevelopingbrain using eeg and eye tracking, Scientific data 4 (1) (2017) 1–20

    2017

  118. [145]

    J. F. Cavanagh, A. Napolitano, C. Wu, A. Mueen, The patient reposi- tory for eeg data+ computational tools (pred+ ct), Frontiers in neu- roinformatics 11 (2017) 67

    2017

  119. [146]

    L. M. Alexander, J. Escalera, L. Ai, C. Andreotti, K. Febre, A. Man- gone, N. Vega-Potler, N. Langer, A. Alexander, M. Kovacs, S. Litke, B.O’Hagan,J.Andersen,B.Bronstein,A.Bui,M.Bushey,H.Butler, V. Castagna, N. Camacho, E. Chan, D. Citera, J. Clucas, S. Co- hen, S. Dufek, M. E...

    2017

  120. [147]

    J. F. Cavanagh, A. W. Bismark, M. J. Frank, J. J. Allen, Multiple dissociationsbetweencomorbiddepressionandanxietyonrewardand punishment processing: Evidence from computationally informed eeg, Computational Psychiatry 3 (2019) 1–17

    2019

  121. [148]

    Jiang, G.-B

    X. Jiang, G.-B. Bian, Z. Tian, Removal of artifacts from eeg signals: a review, Sensors 19 (5) (2019) 987

    2019

  122. [149]

    A. K. Jain, J. Mao, K. M. Mohiuddin, Artificial neural networks: A tutorial, Computer 29 (3) (1996) 31–44

    1996

  123. [150]

    ollah Ansari, M

    A. ollah Ansari, M. Khalili, Diagnosis of major depressive disorder with neural network models, International Journal of Electronics Communication and Computer Engineering (IJECCE) 5 (5) (2014) 1183–1186

    2014

  124. [151]

    M.Bachmann,L.Päeske,K.Kalev,K.Aarma,A.Lehtmets,P.Ööpik, J. Lass, H. Hinrikus, Methods for classifying depression in single channel eeg using linear and nonlinear signal analysis, Computer methods and programs in biomedicine 155 (2018) 11–17

    2018

  125. [152]

    Muzammel, H

    M. Muzammel, H. Salam, Y. Hoffmann, M. Chetouani, A. Othmani, Audvowelconsnet: A phoneme-level based deep cnn architecture for clinical depression diagnosis, Machine Learning with Applications 2 (2020) 100005

    2020

  126. [153]

    Mosavi, S

    A. Mosavi, S. Ardabili, A. R. Varkonyi-Koczy, List of deep learn- ing models, in: International Conference on Global Research and Education, Springer, 2019, pp. 202–214

    2019

  127. [154]

    Craik, Y

    A. Craik, Y. He, J. L. Contreras-Vidal, Deep learning for electroen- cephalogram (eeg) classification tasks: a review, Journal of neural engineering 16 (3) (2019) 031001

    2019

  128. [156]

    Mitra, S

    S. Mitra, S. N. Sarbadhikari, S. K. Pal, An mlp-based model for identifying qeeg in depression, International journal of bio-medical computing 43 (3) (1996) 179–187

    1996

  129. [157]

    U. R. Acharya, V. K. Sudarshan, H. Adeli, J. Santhosh, J. E. Koh, A. Adeli, Computer-aided diagnosis of depression using eeg signals, European neurology 73 (5-6) (2015) 329–336

    2015

  130. [159]

    Xiaolong, W

    Y. Xiaolong, W. Xiaoyun, J. Xinyi, L. Hongbo, Classification of de- pression with brain network characteristics based on multiphase map deep neural network equilibrium compensation, Journal of Medical Imaging and Health Informatics 10 (1) (2020) 134–138

    2020

  131. [160]

    Wright, C

    P.B.Mitchell,A.Frankland,D.Hadzi-Pavlovic,G.Roberts,J.Corry, A. Wright, C. K. Loo, M. Breakspear, Comparison of depressive episodes in bipolar disorder and in major depressive disorder within bipolar disorder pedigrees, The British Journal of Psychiatry 199 (4) (2011) 303–309

    2011

  132. [161]

    L.Samalin,F.Bellivier,B.Giordana,L.Yon,V.Milhiet,W.El-Hage, P.Courtet, E.Hacques, N.Bedira, A.Dillenschneider, etal., Patients’ perspectives on residual symptoms in bipolar disorder: a focus group study, The Journal of nervous and mental disease 202 (7) (2014) 550–555

    2014

  133. [162]

    T. J. L. Kristeen Cherney, Effects of bipolar disorder on the body (October 25, 2018). URL https://www.healthline.com/health/bipolar-disorder/ effects-on-the-body#1

    2018

  134. [163]

    M.DeHert,C.U.Correll,J.Bobes,M.Cetkovich-Bakmas,D.Cohen, I. Asai, J. Detraux, S. Gautam, H.-J. Möller, D. M. Ndetei, et al., Physicalillnessinpatientswithseverementaldisorders.i.prevalence, impactofmedicationsanddisparitiesinhealthcare,Worldpsychiatry 10 (1) (2011) 52

    2011

  135. [164]

    Angst, A

    J. Angst, A. Gamma, F. Benazzi, V. Ajdacic, D. Eich, W. Rössler, Diagnosticissues inbipolar disorder, European Neuropsychopharma- Sana Yasin et al.:Preprint submitted to Elsevier Page 28 of 29 EEG based Major Depressive disorder and Bipolar disorder detection using Neural Netw...

    2003

  136. [165]

    Young, J

    R. Young, J. Biggs, V. Ziegler, D. Meyer, Young mania rating scale, Handbook of psychiatric measures (2000) 540–542

    2000

  137. [166]

    J. B. Williams, A structured interview guide for the hamilton de- pression rating scale, Archives of general psychiatry 45 (8) (1988) 742–747

    1988

  138. [167]

    M. B. First, Structured clinical interview for the dsm (scid), The encyclopedia of clinical psychology (2014) 1–6

    2014

  139. [168]

    Kapczinski, F

    F. Kapczinski, F. Dal-Pizzol, A. L. Teixeira, P. V. Magalhaes, M. Kauer-Sant’Anna, F. Klamt, J. C. F. Moreira, M. A. de Bitten- court Pasquali, G. R. Fries, J. Quevedo, et al., Peripheral biomarkers andillnessactivityinbipolardisorder,Journalofpsychiatricresearch 45 (2) (2011) 156–161

    2011

  140. [169]

    Scola, A

    G. Scola, A. C. Andreazza, The role of neurotrophins in bipolar disorder, Progress in Neuro-Psychopharmacology and Biological Psychiatry 56 (2015) 122–128

    2015

  141. [170]

    Leite, R

    B.S.Fernandes,M.L.Molendijk,C.A.Köhler,J.C.Soares,C.M.G. Leite, R. Machado-Vieira, T. L. Ribeiro, J. C. Silva, P. M. Sales, J.Quevedo,etal.,Peripheralbrain-derivedneurotrophicfactor(bdnf) as a biomarker in bipolar disorder: a meta-analysis of 52 studies, BMC medicine 13 (1) (2015) 289

    2015

  142. [171]

    A. C. Andreazza, M. Kauer-Sant’Anna, B. N. Frey, D. J. Bond, F. Kapczinski, L. T. Young, L. N. Yatham, Oxidative stress markers in bipolar disorder: a meta-analysis, Journal of affective disorders 111 (2-3) (2008) 135–144

    2008

  143. [172]

    Benedetti, V

    F. Benedetti, V. Aggio, M. L. Pratesi, G. Greco, R. Furlan, Neuroin- flammation in bipolar depression, Frontiers in Psychiatry 11 (2020) 71

    2020

  144. [173]

    /uni0130

    M. /uni0130. Atagün, Brain oscillations in bipolar disorder and lithium- induced changes, Neuropsychiatric disease and treatment 12 (2016) 589

    2016

  145. [174]

    A.Khaleghi,A.Sheikhani,M.R.Mohammadi,A.M.Nasrabadi,S.R. Vand, H. Zarafshan, M. Moeini, Eeg classification of adolescents with type i and type ii of bipolar disorder, Australasian physical & engineering sciences in medicine 38 (4) (2015) 551–559

    2015

  146. [175]

    Metin, O

    S.Z.Metin,T.T.Erguzel,G.Ertan,C.Salcini,B.Kocarslan,M.Cebi, B. Metin, O. Tanridag, N. Tarhan, The use of quantitative eeg for dif- ferentiatingfrontotemporaldementiafromlate-onsetbipolardisorder, Clinical EEG and Neuroscience 49 (3) (2018) 171–176

    2018

  147. [176]

    M.Zelenina,D.Prata,Machinelearningwithelectroencephalography features for precise diagnosis of depression subtypes, arXiv preprint arXiv:1908.11217 (2019)

    2019 arXiv

  148. [177]

    M. B. Fonseca, R. S. de Andrades, S. de Lima Bach, C. D. Wiener, J. P. Oses, et al., Bipolar and schizophrenia disorders diagnosis using artificial neural network, Neuroscience and Medicine 9 (04) (2018) 209

    2018

  149. [178]

    T. T. Erguzel, C. Uyulan, B. Unsalver, A. Evrensel, M. Cebi, C. O. Noyan, B. Metin, G. Eryilmaz, G. H. Sayar, N. Tarhan, Entropy: A promising eeg biomarker dichotomizing subjects with opioid use disorderandhealthycontrols,ClinicalEEGandNeuroscience(2020) 1550059420905724

    2020

  150. [179]

    M.Seeck,L.Koessler,T.Bast,F.Leijten,C.Michel,C.Baumgartner, B. He, S. Beniczky, The standardized eeg electrode array of the ifcn, Clinical Neurophysiology 128 (10) (2017) 2070–2077

    2017

  151. [180]

    J. W. Kam, S. Griffin, A. Shen, S. Patel, H. Hinrichs, H.-J. Heinze, L. Y. Deouell, R. T. Knight, Systematic comparison between a wire- less eeg system with dry electrodes and a wired eeg system with wet electrodes, NeuroImage 184 (2019) 119–129

    2019

  152. [181]

    C. Wu, I. MacLeod, A. I. Su, Biogps and mygene. info: organizing online, gene-centric information, Nucleic acids research 41 (D1) (2013) D561–D565

    2013

  153. [182]

    M. L. Phillips, H. A. Swartz, A critical appraisal of neuroimaging studies of bipolar disorder: toward a new conceptualization of under- lying neural circuitry and a road map for future research, American Journal of Psychiatry 171 (8) (2014) 829–843

    2014

  154. [183]

    Potash, J

    J. Potash, J. Toolan, J. Steele, E. Miller, J. Pearl, P. Zandi, T. Schulze, L. Kassem, S. Simpson, V. Lopez, D. MacKinnon, F. Mcmahon, The bipolar disorder phenome database: A resource for genetic studies, The American journal of psychiatry 164 (2007) 1229–37.doi:10. 1176/appi...

    2007 arXiv

  155. [184]

    F. K. Goodwin, K. R. Jamison, Manic-depressive illness: bipolar disorders and recurrent depression, Vol. 1, Oxford University Press, 2007

    2007

  156. [185]

    Rejaibi, A

    E. Rejaibi, A. Komaty, F. Meriaudeau, S. Agrebi, A. Othmani, Mfcc-based recurrent neural network for automatic clinical de- pression recognition and assessment from speech, arXiv preprint arXiv:1909.07208 (2019)

    2019 arXiv

  157. [186]

    N. M. N. Leite, E. T. Pereira, E. C. Gurjao, L. R. Veloso, Deep convolutional autoencoder for eeg noise filtering, in: 2018 IEEE In- ternational Conference on Bioinformatics and Biomedicine (BIBM), IEEE, 2018, pp. 2605–2612

    2018

  158. [187]

    Samek, T

    W. Samek, T. Wiegand, K.-R. Müller, Explainable artificial intel- ligence: Understanding, visualizing and interpreting deep learning models, arXiv preprint arXiv:1708.08296 (2017). Sana Yasin et al.:Preprint submitted to Elsevier Page 29 of 29

    2017 arXiv

Pith tools

Reviewed August 27, 2026 · model on record in the stance chip above.