Survey of Deep Learning and Machine Learning Approaches for Major Depressive Disorder Detection Using EEG Data Article Swipe
Sumathi Balakrishnan
,
Raja Kumar Murugesan
,
Eng Lye Lim
,
Amna Faisal
,
Humaira Ashraf
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1155/jece/6277690
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1155/jece/6277690
Major depressive disorder (MDD) is a common mental health illness in which the affected person experiences chronic sadness and loses interest in activities. Traditionally, MDD is diagnosed using clinical examinations and self‐report questionnaires, both of which are subjective and susceptible to error. Recent advances in electroencephalogram (EEG) analysis have combined physiological markers with machine learning (ML) and deep learning (DL) techniques, offering objective, noninvasive alternatives for MDD detection. Despite numerous studies leveraging ML and DL for EEG‐based MDD identification, there is a lack of comprehensive analysis that critically evaluates the strengths and limitations of these methods. This survey paper addresses this gap by analyzing 31 studies published between 2020 and 2024, focusing on critical aspects such as dataset size, electrode configurations, preprocessing techniques, feature engineering, and the ML/DL models employed. The paper presents a taxonomy that categorizes the methods used across various studies for each step of the EEG‐based depression detection pipeline and an analysis of the critical dimensions required for detecting depression based on the comprehensive analysis of dataset, suitable EEG electrode configuration, effective preprocessing techniques, and feature engineering for different model types. With such insights, this survey aims to guide future research and improve the accuracy and reliability of EEG‐based MDD detection.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/jece/6277690
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1155/jece/6277690
- OA Status
- gold
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- 1
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- OpenAlex ID
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https://doi.org/10.1155/jece/6277690Digital Object Identifier
- Title
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Survey of Deep Learning and Machine Learning Approaches for Major Depressive Disorder Detection Using EEG DataWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-01-01Full publication date if available
- Authors
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Sumathi Balakrishnan, Raja Kumar Murugesan, Eng Lye Lim, Amna Faisal, Humaira AshrafList of authors in order
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https://doi.org/10.1155/jece/6277690Publisher landing page
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1155/jece/6277690Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1155/jece/6277690Direct OA link when available
- Concepts
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Electroencephalography, Major depressive disorder, Artificial intelligence, Deep learning, Computer science, Machine learning, Psychology, Pattern recognition (psychology), Psychiatry, CognitionTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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| abstract_inverted_index.model | 182 |
| abstract_inverted_index.paper | 98, 131 |
| abstract_inverted_index.size, | 118 |
| abstract_inverted_index.there | 79 |
| abstract_inverted_index.these | 94 |
| abstract_inverted_index.using | 27 |
| abstract_inverted_index.which | 11, 35 |
| abstract_inverted_index.Recent | 42 |
| abstract_inverted_index.across | 140 |
| abstract_inverted_index.common | 6 |
| abstract_inverted_index.error. | 41 |
| abstract_inverted_index.future | 192 |
| abstract_inverted_index.health | 8 |
| abstract_inverted_index.mental | 7 |
| abstract_inverted_index.models | 128 |
| abstract_inverted_index.person | 14 |
| abstract_inverted_index.survey | 97, 188 |
| abstract_inverted_index.types. | 183 |
| abstract_inverted_index.Despite | 68 |
| abstract_inverted_index.aspects | 114 |
| abstract_inverted_index.between | 107 |
| abstract_inverted_index.chronic | 16 |
| abstract_inverted_index.dataset | 117 |
| abstract_inverted_index.feature | 123, 178 |
| abstract_inverted_index.illness | 9 |
| abstract_inverted_index.improve | 195 |
| abstract_inverted_index.machine | 53 |
| abstract_inverted_index.markers | 51 |
| abstract_inverted_index.methods | 138 |
| abstract_inverted_index.sadness | 17 |
| abstract_inverted_index.studies | 70, 105, 142 |
| abstract_inverted_index.various | 141 |
| abstract_inverted_index.accuracy | 197 |
| abstract_inverted_index.advances | 43 |
| abstract_inverted_index.affected | 13 |
| abstract_inverted_index.analysis | 47, 85, 154, 167 |
| abstract_inverted_index.clinical | 28 |
| abstract_inverted_index.combined | 49 |
| abstract_inverted_index.critical | 113, 157 |
| abstract_inverted_index.dataset, | 169 |
| abstract_inverted_index.disorder | 2 |
| abstract_inverted_index.focusing | 111 |
| abstract_inverted_index.interest | 20 |
| abstract_inverted_index.learning | 54, 58 |
| abstract_inverted_index.methods. | 95 |
| abstract_inverted_index.numerous | 69 |
| abstract_inverted_index.offering | 61 |
| abstract_inverted_index.pipeline | 151 |
| abstract_inverted_index.presents | 132 |
| abstract_inverted_index.required | 159 |
| abstract_inverted_index.research | 193 |
| abstract_inverted_index.suitable | 170 |
| abstract_inverted_index.taxonomy | 134 |
| abstract_inverted_index.addresses | 99 |
| abstract_inverted_index.analyzing | 103 |
| abstract_inverted_index.detecting | 161 |
| abstract_inverted_index.detection | 150 |
| abstract_inverted_index.diagnosed | 26 |
| abstract_inverted_index.different | 181 |
| abstract_inverted_index.effective | 174 |
| abstract_inverted_index.electrode | 119, 172 |
| abstract_inverted_index.employed. | 129 |
| abstract_inverted_index.evaluates | 88 |
| abstract_inverted_index.insights, | 186 |
| abstract_inverted_index.published | 106 |
| abstract_inverted_index.strengths | 90 |
| abstract_inverted_index.critically | 87 |
| abstract_inverted_index.depression | 149, 162 |
| abstract_inverted_index.depressive | 1 |
| abstract_inverted_index.detection. | 67, 203 |
| abstract_inverted_index.dimensions | 158 |
| abstract_inverted_index.leveraging | 71 |
| abstract_inverted_index.objective, | 62 |
| abstract_inverted_index.subjective | 37 |
| abstract_inverted_index.EEG‐based | 76, 148, 201 |
| abstract_inverted_index.activities. | 22 |
| abstract_inverted_index.categorizes | 136 |
| abstract_inverted_index.engineering | 179 |
| abstract_inverted_index.experiences | 15 |
| abstract_inverted_index.limitations | 92 |
| abstract_inverted_index.noninvasive | 63 |
| abstract_inverted_index.reliability | 199 |
| abstract_inverted_index.susceptible | 39 |
| abstract_inverted_index.techniques, | 60, 122, 176 |
| abstract_inverted_index.alternatives | 64 |
| abstract_inverted_index.engineering, | 124 |
| abstract_inverted_index.examinations | 29 |
| abstract_inverted_index.comprehensive | 84, 166 |
| abstract_inverted_index.physiological | 50 |
| abstract_inverted_index.preprocessing | 121, 175 |
| abstract_inverted_index.self‐report | 31 |
| abstract_inverted_index.Traditionally, | 23 |
| abstract_inverted_index.configuration, | 173 |
| abstract_inverted_index.configurations, | 120 |
| abstract_inverted_index.identification, | 78 |
| abstract_inverted_index.questionnaires, | 32 |
| abstract_inverted_index.electroencephalogram | 45 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.87030433 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |