{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T08:31:24Z","timestamp":1790843484240,"version":"4.1.0"},"reference-count":117,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,11,11]],"date-time":"2024-11-11T00:00:00Z","timestamp":1731283200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100015539","name":"Australian Government","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100015539","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001795","name":"University of Southern Queensland","doi-asserted-by":"publisher","award":["2023-2026"],"award-info":[{"award-number":["2023-2026"]}],"id":[{"id":"10.13039\/501100001795","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computer Methods and Programs in Biomedicine"],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1016\/j.cmpb.2024.108506","type":"journal-article","created":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T15:50:19Z","timestamp":1731426619000},"page":"108506","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":23,"special_numbering":"C","title":["Deep learning techniques for automated Alzheimer's and mild cognitive impairment disease using EEG signals: A comprehensive review of the last decade (2013 - 2024)"],"prefix":"10.1016","volume":"259","author":[{"given":"Madhav","family":"Acharya","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2290-6749","authenticated-orcid":false,"given":"Ravinesh C","family":"Deo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohui","family":"Tao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Prabal Datta","family":"Barua","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aruna","family":"Devi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anirudh","family":"Atmakuru","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ru-San","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.cmpb.2024.108506_bib0001","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.108918","article-title":"A machine-learning approach for stress detection using wearable sensors in free-living environments","volume":"179","author":"Abd Al-Alim","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.cmpb.2024.108506_bib0038","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1016\/j.cger.2014.04.001","article-title":"Dementia and cognitive impairment: epidemiology, diagnosis, and treatment","volume":"30","author":"Hugo","year":"2014","journal-title":"Clin. Geriatr. Med."},{"key":"10.1016\/j.cmpb.2024.108506_bib0116","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1097\/00002093-200304000-00002","article-title":"The magnitude of dementia occurrence in the world","volume":"17","author":"Wimo","year":"2003","journal-title":"Alzheimer Dis. Assoc. Disorders"},{"key":"10.1016\/j.cmpb.2024.108506_bib0078","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1212\/CON.0000000000000313","article-title":"Mild cognitive impairment","volume":"22","author":"Petersen","year":"2016","journal-title":"Continuum: lifelong Learn. Neurol."},{"key":"10.1016\/j.cmpb.2024.108506_bib0044","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1177\/1533317518791401","article-title":"Mild cognitive impairment in clinical practice: a review article","volume":"33","author":"Jongsiriyanyong","year":"2018","journal-title":"Am. J. Alzheimer's Dis. Other Dementias\u00ae"},{"key":"10.1016\/j.cmpb.2024.108506_bib0066","doi-asserted-by":"crossref","first-page":"743","DOI":"10.3233\/JAD-201043","article-title":"Prevalence and risk of mild cognitive impairment in low and middle-income countries: a systematic review","volume":"79","author":"McGrattan","year":"2021","journal-title":"J. Alzheimer's Dis."},{"key":"10.1016\/j.cmpb.2024.108506_bib0025","doi-asserted-by":"crossref","first-page":"3141","DOI":"10.2174\/0929867323666161213101126","article-title":"Alzheimer's disease: a review from the pathophysiology to diagnosis, new perspectives for pharmacological treatment","volume":"25","author":"dos Santos","year":"2018","journal-title":"Curr. Med. Chem."},{"key":"10.1016\/j.cmpb.2024.108506_bib0071","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1002\/ajmg.b.32499","article-title":"Genomic variants, genes, and pathways of Alzheimer's disease: An overview","volume":"174","author":"Naj","year":"2017","journal-title":"Am. J. Med. Genet. Part B: Neuropsychiatr. Genet."},{"key":"10.1016\/j.cmpb.2024.108506_bib0016","doi-asserted-by":"crossref","first-page":"2107","DOI":"10.1016\/j.neurobiolaging.2011.05.023","article-title":"Genetic variation in APOE cluster region and Alzheimer's disease risk","volume":"32","author":"Cervantes","year":"2011","journal-title":"Neurobiol. Aging"},{"key":"10.1016\/j.cmpb.2024.108506_bib0008","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1034\/j.1600-0404.107.s179.7.x","article-title":"Neuropsychological features of mild cognitive impairment and preclinical Alzheimer's disease","volume":"107","author":"Arn\u00e1iz","year":"2003","journal-title":"Acta Neurol. Scand."},{"key":"10.1016\/j.cmpb.2024.108506_bib0095","doi-asserted-by":"crossref","first-page":"8","DOI":"10.3389\/fnagi.2013.00008","article-title":"Does combined cognitive training and physical activity training enhance cognitive abilities more than either alone? A four-condition randomized controlled trial among healthy older adults","volume":"5","author":"Shatil","year":"2013","journal-title":"Front. Aging Neurosci."},{"key":"10.1016\/j.cmpb.2024.108506_bib0106","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1136\/jnnp.2003.028175","article-title":"Applications of positron emission tomography (PET) in neurology","volume":"75","author":"Tai","year":"2004","journal-title":"J. Neurol. Neurosurg. Psychiatry"},{"key":"10.1016\/j.cmpb.2024.108506_bib0063","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1007\/978-3-030-53168-3_12","article-title":"Neuroimaging findings in mild cognitive impairment","author":"Massa","year":"2021","journal-title":"PET and SPECT in Neurol."},{"key":"10.1016\/j.cmpb.2024.108506_bib0022","doi-asserted-by":"crossref","first-page":"487","DOI":"10.2174\/156720510792231720","article-title":"Diagnosis of Alzheimer's disease from EEG signals: where are we standing?","volume":"7","author":"Dauwels","year":"2010","journal-title":"Curr. Alzheimer. Res."},{"key":"10.1016\/j.cmpb.2024.108506_bib0067","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1016\/j.neuroimage.2011.12.039","article-title":"Towards the utilization of EEG as a brain imaging tool","volume":"61","author":"Michel","year":"2012","journal-title":"Neuroimage"},{"key":"10.1016\/j.cmpb.2024.108506_bib0086","first-page":"2309","article-title":"Analysis of EEG signals using nonlinear dynamics and chaos: a review","volume":"9","author":"Rodriguez-Bermudez","year":"2015","journal-title":"Appl. Math. Inf. Sci."},{"key":"10.1016\/j.cmpb.2024.108506_bib0014","first-page":"1982","article-title":"Alzheimer's disease diagnosis and severity level detection based on electroencephalography modulation spectral \u201cpatch\u201d features","volume":"24","author":"Cassani","year":"2019","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.cmpb.2024.108506_bib0056","doi-asserted-by":"crossref","first-page":"1894","DOI":"10.3390\/app10051894","article-title":"A survey on computer-aided diagnosis of brain disorders through MRI based on machine learning and data mining methodologies with an emphasis on Alzheimer disease diagnosis and the contribution of the multimodal fusion","volume":"10","author":"Lazli","year":"2020","journal-title":"Appl. Sci."},{"key":"10.1016\/j.cmpb.2024.108506_bib0072","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.patcog.2015.08.027","article-title":"Human detection from images and videos: A survey","volume":"51","author":"Nguyen","year":"2016","journal-title":"Pattern. Recognit."},{"key":"10.1016\/j.cmpb.2024.108506_bib0100","first-page":"141","article-title":"EEG signal analysis and classification","volume":"11","author":"Siuly","year":"2016","journal-title":"IEEE Trans. Neural Syst. Rehabilit. Eng."},{"key":"10.1016\/j.cmpb.2024.108506_bib0082","doi-asserted-by":"crossref","first-page":"58","DOI":"10.3390\/brainsci7060058","article-title":"A review of issues related to data acquisition and analysis in EEG\/MEG studies","volume":"7","author":"Puce","year":"2017","journal-title":"Brain Sci."},{"key":"10.1016\/j.cmpb.2024.108506_bib0089","doi-asserted-by":"crossref","first-page":"477","DOI":"10.3390\/diagnostics13030477","article-title":"An approach toward artificial intelligence Alzheimer's disease diagnosis using brain signals","volume":"13","author":"Sadegh-Zadeh","year":"2023","journal-title":"diagnostics"},{"key":"10.1016\/j.cmpb.2024.108506_bib0064","series-title":"Intelligent Systems for Science and Information: Extended and Selected Results from the Science and Information Conference","first-page":"121","article-title":"Novel approaches to developing multimodal biometric systems with autonomic liveness detection characteristics","volume":"2013","author":"Matthew","year":"2014"},{"key":"10.1016\/j.cmpb.2024.108506_bib0017","article-title":"Recent advances in bio-integrated electrochemical sensors for neuroengineering","author":"Chen","year":"2023","journal-title":"Fundam. Res."},{"key":"10.1016\/j.cmpb.2024.108506_bib0065","article-title":"Non-invasive brain stimulation for patients and healthy subjects: Current challenges and future perspectives","author":"Mattioli","year":"2023","journal-title":"J. Neurol. Sci."},{"key":"10.1016\/j.cmpb.2024.108506_bib0084","first-page":"1","article-title":"Diagnosis and prognosis of mental disorders by means of EEG and deep learning: a systematic mapping study","author":"Rivera","year":"2022","journal-title":"Artif. Intell. Rev."},{"key":"10.1016\/j.cmpb.2024.108506_bib0121","doi-asserted-by":"crossref","first-page":"2924","DOI":"10.1109\/TBME.2019.2898871","article-title":"M\/EEG-based bio-markers to predict the MCI and Alzheimer's disease: a review from the ML perspective","volume":"66","author":"Yang","year":"2019","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"10.1016\/j.cmpb.2024.108506_bib0109","doi-asserted-by":"crossref","DOI":"10.1142\/S0129065721300023","article-title":"Machine learning algorithms and statistical approaches for Alzheimer's disease analysis based on resting-state EEG recordings: A systematic review","volume":"31","author":"Tzimourta","year":"2021","journal-title":"Int. J. Neural Syst."},{"key":"10.1016\/j.cmpb.2024.108506_bib0062","doi-asserted-by":"crossref","first-page":"5122","DOI":"10.1109\/TNNLS.2018.2791644","article-title":"Permutation Jaccard distance-based hierarchical clustering to estimate EEG network density modifications in MCI subjects","volume":"29","author":"Mammone","year":"2018","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.cmpb.2024.108506_bib0023","first-page":"8348","article-title":"Remote neuro-cognitive impairment sensing based on P300 spatio-temporal monitoring","volume":"16","author":"De Venuto","year":"2016","journal-title":"IEEE Sens. J."},{"key":"10.1016\/j.cmpb.2024.108506_bib0057","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.104349","article-title":"A mild cognitive impairment diagnostic model based on IAAFT and BiLSTM","volume":"80","author":"Li","year":"2023","journal-title":"Biomed. Signal. Process. Control"},{"key":"10.1016\/j.cmpb.2024.108506_bib0007","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2022.108815","article-title":"A deep learning based framework for diagnosis of mild cognitive impairment","volume":"248","author":"Alvi","year":"2022","journal-title":"Knowl. Based. Syst."},{"key":"10.1016\/j.cmpb.2024.108506_bib0006","doi-asserted-by":"crossref","first-page":"12483","DOI":"10.1038\/s41598-024-63180-y","article-title":"Selecting EEG channels and features using multi-objective optimization for accurate MCI detection: validation using leave-one-subject-out strategy","volume":"14","author":"Aljalal","year":"2024","journal-title":"Sci. Rep."},{"key":"10.1016\/j.cmpb.2024.108506_bib0026","doi-asserted-by":"crossref","first-page":"2164","DOI":"10.1109\/TNSRE.2020.3014951","article-title":"Topological network analysis of early Alzheimer's disease based on resting-state EEG","volume":"28","author":"Duan","year":"2020","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"10.1016\/j.cmpb.2024.108506_bib0118","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1109\/TCDS.2021.3054504","article-title":"Resting-state brain connectivity via multivariate EMD in mild cognitive impairment","volume":"14","author":"Wu","year":"2021","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"key":"10.1016\/j.cmpb.2024.108506_bib0043","doi-asserted-by":"crossref","first-page":"389","DOI":"10.3233\/JAD-190628","article-title":"A novel detection tool for mild cognitive impairment patients based on eye movement and electroencephalogram","volume":"72","author":"Jiang","year":"2019","journal-title":"J. Alzheimer's Dis."},{"key":"10.1016\/j.cmpb.2024.108506_bib0013","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.gerinurse.2022.08.001","article-title":"Effects of mindfulness in patients with mild cognitive impairment with insomnia: A double-blind randomized controlled trial","volume":"47","author":"Cai","year":"2022","journal-title":"Geriatr. Nurs. (Minneap)"},{"key":"10.1016\/j.cmpb.2024.108506_bib0104","doi-asserted-by":"crossref","first-page":"1916","DOI":"10.1109\/LRA.2019.2898470","article-title":"Robot-based training for people with mild cognitive impairment","volume":"4","author":"Stogl","year":"2019","journal-title":"IEEE Robot. Autom. Lett."},{"key":"10.1016\/j.cmpb.2024.108506_bib0126","doi-asserted-by":"crossref","first-page":"902","DOI":"10.1109\/TBCAS.2022.3198265","article-title":"EMCI: a novel EEG-based mental workload assessment index of mild cognitive impairment","volume":"16","author":"Zeng","year":"2022","journal-title":"IEEE Trans. Biomed. Circuits. Syst."},{"key":"10.1016\/j.cmpb.2024.108506_bib0011","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.neunet.2019.02.005","article-title":"Early Alzheimer's disease diagnosis based on EEG spectral images using deep learning","volume":"114","author":"Bi","year":"2019","journal-title":"Neural Netw."},{"key":"10.1016\/j.cmpb.2024.108506_bib0081","doi-asserted-by":"crossref","DOI":"10.1142\/S0129065722500228","article-title":"Dynamics of the \u201ccognitive\u201d brain wave P3b at rest for Alzheimer dementia prediction in mild cognitive impairment","volume":"32","author":"Porcaro","year":"2022","journal-title":"Int. J. Neural Syst."},{"key":"10.1016\/j.cmpb.2024.108506_bib0117","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1109\/TCDS.2020.3015131","article-title":"Detecting Alzheimer's dementia degree","volume":"14","author":"Wu","year":"2020","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"key":"10.1016\/j.cmpb.2024.108506_bib0123","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1109\/TFUZZ.2019.2903753","article-title":"Supervised network-based fuzzy learning of EEG signals for Alzheimer's disease identification","volume":"28","author":"Yu","year":"2019","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"10.1016\/j.cmpb.2024.108506_bib0101","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1109\/TBME.2018.2834546","article-title":"Biomarkers for Alzheimer's disease defined by a novel brain functional network measure","volume":"66","author":"Song","year":"2018","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"10.1016\/j.cmpb.2024.108506_bib0047","doi-asserted-by":"crossref","first-page":"e12347","DOI":"10.1002\/trc2.12347","article-title":"Home EEG sleep assessment shows reduced slow-wave sleep in mild\u2013moderate Alzheimer's disease","volume":"8","author":"Kent","year":"2022","journal-title":"Alzheimer's Dementia: Transl. Res. Clin. Interv."},{"key":"10.1016\/j.cmpb.2024.108506_bib0004","doi-asserted-by":"crossref","first-page":"949","DOI":"10.1109\/JBHI.2016.2578954","article-title":"Directed functional networks in Alzheimer's disease: disruption of global and local connectivity measures","volume":"21","author":"Afshari","year":"2016","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.cmpb.2024.108506_bib0052","doi-asserted-by":"crossref","first-page":"2651","DOI":"10.1109\/TNSRE.2022.3204913","article-title":"EEG-based graph neural network classification of Alzheimer's disease: An empirical evaluation of functional connectivity methods","volume":"30","author":"Klepl","year":"2022","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"10.1016\/j.cmpb.2024.108506_bib0060","doi-asserted-by":"crossref","DOI":"10.1155\/2023\/3198066","article-title":"Using CNN Saliency Maps and EEG Modulation Spectra for Improved and More Interpretable Machine Learning-Based Alzheimer's Disease Diagnosis","volume":"2023","author":"Lopes","year":"2023","journal-title":"Comput. Intell. Neurosci."},{"key":"10.1016\/j.cmpb.2024.108506_bib0092","doi-asserted-by":"crossref","first-page":"5194","DOI":"10.1002\/hbm.25994","article-title":"Spatial\u2013temporal graph convolutional network for Alzheimer classification based on brain functional connectivity imaging of electroencephalogram","volume":"43","author":"Shan","year":"2022","journal-title":"Hum. Brain Mapp."},{"key":"10.1016\/j.cmpb.2024.108506_bib0040","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.105298","article-title":"Alzheimer's diseases diagnosis using fusion of high informative BiLSTM and CNN features of EEG signal","volume":"86","author":"Imani","year":"2023","journal-title":"Biomed. Signal. Process. Control"},{"key":"10.1016\/j.cmpb.2024.108506_bib0087","first-page":"10","article-title":"Inter-hospital moderate and advanced Alzheimer's disease detection through convolutional neural networks","author":"Roncero-Parra","year":"2024","journal-title":"Heliyon"},{"key":"10.1016\/j.cmpb.2024.108506_bib0037","doi-asserted-by":"crossref","DOI":"10.1088\/1741-2552\/ac05d8","article-title":"Deep learning of resting-state electroencephalogram signals for three-class classification of Alzheimer's disease, mild cognitive impairment and healthy ageing","volume":"18","author":"Huggins","year":"2021","journal-title":"J. Neural Eng."},{"key":"10.1016\/j.cmpb.2024.108506_bib0029","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.105266","article-title":"Identification of Alzheimer's disease from central lobe EEG signals utilizing machine learning and residual neural network","volume":"86","author":"Fouad","year":"2023","journal-title":"Biomed. Signal. Process. Control"},{"key":"10.1016\/j.cmpb.2024.108506_bib0055","article-title":"EEGAlzheimer'sNet: Development of transformer-based attention long short term memory network for detecting Alzheimer disease using EEG signal","volume":"86","author":"kumar Ravikanti","year":"2023","journal-title":"Biomed. Signal. Process. Control"},{"key":"10.1016\/j.cmpb.2024.108506_bib0049","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2023.110858","article-title":"Adazd-Net: Automated adaptive and explainable Alzheimer's disease detection system using EEG signals","volume":"278","author":"Khare","year":"2023","journal-title":"Knowl. Based. Syst."},{"key":"10.1016\/j.cmpb.2024.108506_bib0102","doi-asserted-by":"crossref","DOI":"10.1088\/1741-2552\/ac697d","article-title":"An EEG-based systematic explainable detection framework for probing and localizing abnormal patterns in Alzheimer's disease","volume":"19","author":"Song","year":"2022","journal-title":"J. Neural Eng."},{"key":"10.1016\/j.cmpb.2024.108506_bib0090","doi-asserted-by":"crossref","DOI":"10.1016\/j.jksuci.2023.101848","article-title":"Deep generative adversarial networks with marine predators algorithm for classification of Alzheimer's disease using electroencephalogram","volume":"35","author":"Sekhar","year":"2023","journal-title":"J. King Saud Univ. Comput. Inf. Sci."},{"key":"10.1016\/j.cmpb.2024.108506_bib0068","doi-asserted-by":"crossref","DOI":"10.1109\/ACCESS.2023.3294618","article-title":"DICE-net: a novel convolution-transformer architecture for Alzheimer detection in EEG signals","author":"Miltiadous","year":"2023","journal-title":"IEEE Access"},{"key":"10.1016\/j.cmpb.2024.108506_bib0119","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.104917","article-title":"Efficient identification of Alzheimer's brain dynamics with Spatial-Temporal Autoencoder: A deep learning approach for diagnosing brain disorders","volume":"86","author":"Wu","year":"2023","journal-title":"Biomed. Signal. Process. Control"},{"key":"10.1016\/j.cmpb.2024.108506_bib0036","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0193607","article-title":"Diagnosis of Alzheimer's disease with Electroencephalography in a differential framework","volume":"13","author":"Houmani","year":"2018","journal-title":"PLoS One"},{"key":"10.1016\/j.cmpb.2024.108506_bib0105","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0265484","article-title":"A prospective multicenter validation study of a machine learning algorithm classifier on quantitative electroencephalogram for differentiating between dementia with Lewy bodies and Alzheimer's dementia","volume":"17","author":"Suzuki","year":"2022","journal-title":"PLoS One"},{"key":"10.1016\/j.cmpb.2024.108506_bib0125","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1186\/s12916-023-02811-z","article-title":"NREM sleep as a novel protective cognitive reserve factor in the face of Alzheimer's disease pathology","volume":"21","author":"Zavecz","year":"2023","journal-title":"BMC Med."},{"key":"10.1016\/j.cmpb.2024.108506_bib0020","doi-asserted-by":"crossref","DOI":"10.1016\/j.jneumeth.2020.108618","article-title":"An EEG-fNIRS hybridization technique in the four-class classification of alzheimer's disease","volume":"336","author":"Cicalese","year":"2020","journal-title":"J. Neurosci. Methods"},{"key":"10.1016\/j.cmpb.2024.108506_bib0021","doi-asserted-by":"crossref","first-page":"1643","DOI":"10.1523\/JNEUROSCI.1312-22.2022","article-title":"Temporal irreversibility of large-scale brain dynamics in Alzheimer's disease","volume":"43","author":"Cruzat","year":"2023","journal-title":"J. Neurosci."},{"key":"10.1016\/j.cmpb.2024.108506_bib0075","doi-asserted-by":"crossref","first-page":"29924","DOI":"10.1109\/ACCESS.2019.2899216","article-title":"Automatic detection and diagnosis of neurologic diseases","volume":"7","author":"Nunes","year":"2019","journal-title":"IEEE Access"},{"key":"10.1016\/j.cmpb.2024.108506_bib0074","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.105751","article-title":"A novel hybrid model in the diagnosis and classification of Alzheimer's disease using EEG signals: Deep ensemble learning (DEL) approach","volume":"89","author":"Nour","year":"2024","journal-title":"Biomed. Signal. Process. Control"},{"key":"10.1016\/j.cmpb.2024.108506_bib0034","doi-asserted-by":"crossref","DOI":"10.3389\/fpsyt.2024.1392158","article-title":"Utilizing portable electroencephalography to screen for pathology of Alzheimer's disease: a methodological advancement in diagnosis of neurodegenerative diseases","volume":"15","author":"Hata","year":"2024","journal-title":"Front. Psychiatry"},{"key":"10.1016\/j.cmpb.2024.108506_bib0012","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.artmed.2015.03.003","article-title":"An improved I-FAST system for the diagnosis of Alzheimer's disease from unprocessed electroencephalograms by using robust invariant features","volume":"64","author":"Buscema","year":"2015","journal-title":"Artif. Intell. Med."},{"key":"10.1016\/j.cmpb.2024.108506_bib0027","doi-asserted-by":"crossref","first-page":"826","DOI":"10.1109\/TNSRE.2019.2909100","article-title":"A dementia classification framework using frequency and time-frequency features based on EEG signals","volume":"27","author":"Durongbhan","year":"2019","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"10.1016\/j.cmpb.2024.108506_bib0051","doi-asserted-by":"crossref","first-page":"5252","DOI":"10.1038\/s41598-024-55656-8","article-title":"Electroencephalography-based classification of Alzheimer's disease spectrum during computer-based cognitive testing","volume":"14","author":"Kim","year":"2024","journal-title":"Sci. Rep."},{"key":"10.1016\/j.cmpb.2024.108506_bib0054","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2021.3120131","article-title":"A pilot study on visually stimulated cognitive tasks for EEG-based dementia recognition","volume":"70","author":"Kongwudhikunakorn","year":"2021","journal-title":"IEEe Trans. Instrum. Meas."},{"key":"10.1016\/j.cmpb.2024.108506_bib0093","doi-asserted-by":"crossref","first-page":"1890","DOI":"10.1109\/TNSRE.2020.3007860","article-title":"Iterative filtering decomposition based early dementia diagnosis using EEG with cognitive tests","volume":"28","author":"Sharma","year":"2020","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"10.1016\/j.cmpb.2024.108506_bib0010","doi-asserted-by":"crossref","first-page":"68718","DOI":"10.1109\/ACCESS.2019.2918560","article-title":"Multiscale fluctuation-based dispersion entropy and its applications to neurological diseases","volume":"7","author":"Azami","year":"2019","journal-title":"IEEE Access"},{"key":"10.1016\/j.cmpb.2024.108506_bib0019","doi-asserted-by":"crossref","first-page":"2340","DOI":"10.1109\/TKDE.2018.2820024","article-title":"Inferring cognitive wellness from motor patterns","volume":"30","author":"Chen","year":"2018","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.cmpb.2024.108506_bib0050","doi-asserted-by":"crossref","DOI":"10.1016\/j.neuroimage.2023.120054","article-title":"Deep learning-based EEG analysis to classify normal, mild cognitive impairment, and dementia: Algorithms and dataset","volume":"272","author":"Kim","year":"2023","journal-title":"Neuroimage"},{"key":"10.1016\/j.cmpb.2024.108506_bib0076","doi-asserted-by":"crossref","first-page":"6839","DOI":"10.1109\/TPAMI.2021.3091214","article-title":"Disease-image-specific learning for diagnosis-oriented neuroimage synthesis with incomplete multi-modality data","volume":"44","author":"Pan","year":"2021","journal-title":"IEEE Trans. Pattern. Anal. Mach. Intell."},{"key":"10.1016\/j.cmpb.2024.108506_bib0113","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.neunet.2023.12.009","article-title":"A deep learning model for the detection of various dementia and MCI pathologies based on resting-state electroencephalography data: A retrospective multicentre study","volume":"171","author":"Watanabe","year":"2024","journal-title":"Neural Netw."},{"key":"10.1016\/j.cmpb.2024.108506_bib0098","doi-asserted-by":"crossref","DOI":"10.1088\/1741-2552\/acb96e","article-title":"An attention-based deep learning approach for the classification of subjective cognitive decline and mild cognitive impairment using resting-state EEG","volume":"20","author":"Sibilano","year":"2023","journal-title":"J. Neural Eng."},{"key":"10.1016\/j.cmpb.2024.108506_bib0042","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1007\/s12559-023-10188-7","article-title":"Assessing the Potential of Data Augmentation in EEG Functional Connectivity for Early Detection of Alzheimer's Disease","volume":"16","author":"Jia","year":"2024","journal-title":"Cognit. Comput."},{"key":"10.1016\/j.cmpb.2024.108506_bib0030","doi-asserted-by":"crossref","first-page":"1247","DOI":"10.1007\/s12559-022-10033-3","article-title":"Efficient deep neural networks for classification of Alzheimer's disease and mild cognitive impairment from scalp EEG recordings","volume":"14","author":"Fouladi","year":"2022","journal-title":"Cognit. Comput."},{"key":"10.1016\/j.cmpb.2024.108506_bib0039","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.neucom.2018.09.071","article-title":"A convolutional neural network approach for classification of dementia stages based on 2D-spectral representation of EEG recordings","volume":"323","author":"Ieracitano","year":"2019","journal-title":"Neurocomputing"},{"key":"10.1016\/j.cmpb.2024.108506_bib0107","article-title":"Exploring frequency band-based biomarkers of EEG signals for mild cognitive impairment detection","author":"Tawhid","year":"2023","journal-title":"IEEE Trans.Neural Syst. Rehabil. Eng."},{"key":"10.1016\/j.cmpb.2024.108506_bib0129","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2024.108123","article-title":"STCGRU: A hybrid model based on CNN and BiGRU for mild cognitive impairment diagnosis","volume":"248","author":"Zhou","year":"2024","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.cmpb.2024.108506_bib0122","doi-asserted-by":"crossref","DOI":"10.3389\/fpubh.2020.584387","article-title":"Alzheimer's disease classification with a cascade neural network","volume":"8","author":"You","year":"2020","journal-title":"Front. Public Health"},{"key":"10.1016\/j.cmpb.2024.108506_bib0003","doi-asserted-by":"crossref","DOI":"10.1109\/TNSRE.2024.3374651","article-title":"EEG-based brain functional network analysis for differential identification of dementia-related disorders and their onset","author":"Adebisi","year":"2024","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"10.1016\/j.cmpb.2024.108506_bib0085","doi-asserted-by":"crossref","first-page":"3384","DOI":"10.1109\/JBHI.2021.3069789","article-title":"Lacsogram: A new EEG tool to diagnose Alzheimer's disease","volume":"25","author":"Rodrigues","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.cmpb.2024.108506_bib0096","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1007\/s13755-022-00186-8","article-title":"Microstate feature fusion for distinguishing AD from MCI","volume":"10","author":"Shi","year":"2022","journal-title":"Health Inf. Sci. Syst."},{"key":"10.1016\/j.cmpb.2024.108506_bib0111","doi-asserted-by":"crossref","first-page":"2525","DOI":"10.1109\/TIM.2017.2668618","article-title":"EEG\/ERP: Within episodic assessment framework for cognition","volume":"66","author":"Wallace","year":"2017","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.cmpb.2024.108506_bib0103","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TNSRE.2018.2883514","article-title":"Complex tensor factorization with PARAFAC2 for the estimation of brain connectivity from the EEG","volume":"27","author":"Spyrou","year":"2018","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"10.1016\/j.cmpb.2024.108506_bib0018","doi-asserted-by":"crossref","first-page":"1456","DOI":"10.1109\/TNSRE.2024.3379891","article-title":"A Multi-Modal Classification Method for Early Diagnosis of Mild Cognitive Impairment and Alzheimer's Disease Using Three Paradigms With Various Task Difficulties","volume":"32","author":"Chen","year":"2024","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"10.1016\/j.cmpb.2024.108506_bib0024","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/TTS.2023.3234203","article-title":"Challenges of deep learning in medical image analysis\u2014improving explainability and trust","volume":"4","author":"Dhar","year":"2023","journal-title":"IEEE Trans. Technol. Soc."},{"key":"10.1016\/j.cmpb.2024.108506_bib0108","doi-asserted-by":"crossref","first-page":"2335","DOI":"10.1007\/s00530-022-00960-4","article-title":"A survey on the interpretability of deep learning in medical diagnosis","volume":"28","author":"Teng","year":"2022","journal-title":"Multimed. Syst."},{"key":"10.1016\/j.cmpb.2024.108506_bib0009","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.124665","article-title":"Deep learning in radiology for lung cancer diagnostics: A systematic review of classification, segmentation, and predictive modeling techniques","author":"Atmakuru","year":"2024","journal-title":"Expert. Syst. Appl."},{"key":"10.1016\/j.cmpb.2024.108506_bib0053","doi-asserted-by":"crossref","DOI":"10.1109\/TNSRE.2023.3321634","article-title":"Adaptive gated graph convolutional network for explainable diagnosis of Alzheimer's disease using EEG data","author":"Klepl","year":"2023","journal-title":"IEEE Tran. Neural Syst. Rehabil. Eng."},{"key":"10.1016\/j.cmpb.2024.108506_bib0045","doi-asserted-by":"crossref","first-page":"25","DOI":"10.4103\/2228-7477.175869","article-title":"Automatic diagnosis of mild cognitive impairment using electroencephalogram spectral features","volume":"6","author":"Kashefpoor","year":"2016","journal-title":"J. Med. Signals Sens."},{"key":"10.1016\/j.cmpb.2024.108506_bib0080","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0231169","article-title":"Quantile graphs for EEG-based diagnosis of Alzheimer's disease","volume":"15","author":"Pineda","year":"2020","journal-title":"PLoS One"},{"key":"10.1016\/j.cmpb.2024.108506_bib0015","doi-asserted-by":"crossref","first-page":"2287","DOI":"10.1007\/s11517-021-02427-6","article-title":"Novelty detection-based approach for Alzheimer's disease and mild cognitive impairment diagnosis from EEG","volume":"59","author":"Cejnek","year":"2021","journal-title":"Med. Biol. Eng. Comput."},{"key":"10.1016\/j.cmpb.2024.108506_bib0031","doi-asserted-by":"crossref","DOI":"10.1088\/1741-2560\/12\/1\/016018","article-title":"A hybrid feature selection approach for the early diagnosis of Alzheimer's disease","volume":"12","author":"Gallego-Jutgl\u00e0","year":"2015","journal-title":"J. Neural Eng."},{"key":"10.1016\/j.cmpb.2024.108506_bib0070","doi-asserted-by":"crossref","first-page":"1052","DOI":"10.1016\/S1388-2457(02)00128-1","article-title":"A new EEG method for estimating cortical neuronal impairment that is sensitive to early stage Alzheimer's disease","volume":"113","author":"Musha","year":"2002","journal-title":"Clin. Neurophysiol."},{"key":"10.1016\/j.cmpb.2024.108506_bib0079","doi-asserted-by":"crossref","first-page":"1325","DOI":"10.3233\/JAD-160056","article-title":"Behavioral and electrophysiological correlates of memory binding deficits in patients at different risk levels for Alzheimer's disease","volume":"53","author":"Pietto","year":"2016","journal-title":"J. Alzheimer's Dis."},{"key":"10.1016\/j.cmpb.2024.108506_bib0069","first-page":"88","article-title":"A dataset of EEG recordings from: Alzheimer's disease, Frontotemporal dementia and Healthy subjects","volume":"1","author":"Miltiadous","year":"2023","journal-title":"OpenNeuro"},{"key":"10.1016\/j.cmpb.2024.108506_bib0097","doi-asserted-by":"crossref","DOI":"10.1109\/TNSRE.2023.3329174","article-title":"Differentiating between Alzheimer's Disease and Frontotemporal Dementia Based on the Resting-State Multilayer EEG Network","author":"Si","year":"2023","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"10.1016\/j.cmpb.2024.108506_bib0099","doi-asserted-by":"crossref","DOI":"10.1109\/TETCI.2024.3353610","article-title":"Exploring rhythms and channels-based EEG biomarkers for early detection of alzheimer's disease","author":"Siuly","year":"2024","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"10.1016\/j.cmpb.2024.108506_bib0114","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/j.clinph.2015.05.016","article-title":"Resting-state EEG coupling analysis of amnestic mild cognitive impairment with type 2 diabetes mellitus by using permutation conditional mutual information","volume":"127","author":"Wen","year":"2016","journal-title":"Clin. Neurophysiol."},{"key":"10.1016\/j.cmpb.2024.108506_bib0115","doi-asserted-by":"crossref","first-page":"1702","DOI":"10.1109\/TNSRE.2020.3004462","article-title":"Feature classification method of resting-state EEG signals from amnestic mild cognitive impairment with type 2 diabetes mellitus based on multi-view convolutional neural network","volume":"28","author":"Wen","year":"2020","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"10.1016\/j.cmpb.2024.108506_bib0059","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2022.107161","article-title":"Application of explainable artificial intelligence for healthcare: A systematic review of the last decade (2011\u20132022)","volume":"226","author":"Loh","year":"2022","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.cmpb.2024.108506_bib0002","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.inffus.2021.05.008","article-title":"A review of uncertainty quantification in deep learning: Techniques, applications and challenges","volume":"76","author":"Abdar","year":"2021","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.cmpb.2024.108506_bib0028","series-title":"arXiv preprint arXiv","author":"Fl\u00fcgge","year":"2019"},{"key":"10.1016\/j.cmpb.2024.108506_bib0041","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2022.107308","article-title":"Uncertainty quantification in DenseNet model using myocardial infarction ECG signals","volume":"229","author":"Jahmunah","year":"2023","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.cmpb.2024.108506_bib0112","article-title":"Assisted diagnosis of alzheimer's disease based on deep learning and multimodal feature fusion","volume":"2021","author":"Wang","year":"2021","journal-title":"Complexity"},{"key":"10.1016\/j.cmpb.2024.108506_bib130","series-title":"Attention mechanisms and deep learning for machine vision: A survey of the state of the art","author":"Hafiz","year":"2021"},{"key":"10.1016\/j.cmpb.2024.108506_bib0032","doi-asserted-by":"crossref","first-page":"22106","DOI":"10.1109\/ACCESS.2021.3055280","article-title":"Graph deep learning: State of the art and challenges","volume":"9","author":"Georgousis","year":"2021","journal-title":"IEEe Access"},{"key":"10.1016\/j.cmpb.2024.108506_bib0058","series-title":"arXiv preprint arXiv","author":"Liu","year":"2016"},{"key":"10.1016\/j.cmpb.2024.108506_bib0061","series-title":"arXiv preprint arXiv","author":"Mammen","year":"2021"},{"key":"10.1016\/j.cmpb.2024.108506_bib0124","series-title":"arXiv preprint arXiv","author":"Yuan","year":"2020"},{"key":"10.1016\/j.cmpb.2024.108506_bib0127","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.103770","article-title":"DCML: Deep contrastive mutual learning for COVID-19 recognition","volume":"77","author":"Zhang","year":"2022","journal-title":"Biomed. Signal. Process. Control"},{"key":"10.1016\/j.cmpb.2024.108506_bib0091","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2024.108200","article-title":"All you need is data preparation: A systematic review of image harmonization techniques in Multi-center\/device studies for medical support systems","author":"Seoni","year":"2024","journal-title":"Comput. Methods Programs Biomed."}],"container-title":["Computer Methods and Programs in Biomedicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169260724004991?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169260724004991?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T02:17:30Z","timestamp":1777515450000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0169260724004991"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2]]},"references-count":117,"alternative-id":["S0169260724004991"],"URL":"https:\/\/doi.org\/10.1016\/j.cmpb.2024.108506","relation":{},"ISSN":["0169-2607"],"issn-type":[{"value":"0169-2607","type":"print"}],"subject":[],"published":{"date-parts":[[2025,2]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Deep learning techniques for automated Alzheimer's and mild cognitive impairment disease using EEG signals: A comprehensive review of the last decade (2013 - 2024)","name":"articletitle","label":"Article Title"},{"value":"Computer Methods and Programs in Biomedicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.cmpb.2024.108506","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 The Author(s). Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"108506"}}