{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T14:57:10Z","timestamp":1788274630452,"version":"build-2803163510"},"reference-count":32,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,2,12]],"date-time":"2025-02-12T00:00:00Z","timestamp":1739318400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Korea Institute of Science and Technology (KIST) Institutional Program","award":["2E33771"],"award-info":[{"award-number":["2E33771"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: Alzheimer\u2019s disease (AD) is a progressive neurodegenerative disorder advancing through subjective cognitive decline (SCD), mild cognitive impairment (MCI), and dementia, making early diagnosis crucial. Electroencephalography (EEG) is a non-invasive, cost-effective alternative to advanced neuroimaging for detecting early neural changes. While most studies focus on resting-state EEG or handcrafted features with traditional machine learning, deep learning (DL) offers a promising tool for automated EEG analysis. This study classified the AD spectrum (SCD, MCI, AD) using EEG recorded during resting-state and task-based conditions. Specifically, EEG was recorded during a simple yes\/no question-answering task, mimicking everyday cognitive activities, and was explored. We hypothesized that brain activity during tasks involving listening, comprehension, and response execution provides diagnostic insights. Methods: We collected 1 min of resting-state EEG and approximately 3 min of task-based EEG from 20, 28, and 10 participants with SCD, MCI, and AD, respectively. Task data included response accuracy and reaction time. After minimal preprocessing, two DL models, attention long short-term memory and EEGConformer, were used for binary (e.g., SCD vs. MCI) and three-class (SCD, MCI, AD) classification. Results: Task-based EEG outperformed resting-state EEG, with a 5\u201315% improvement in accuracy. The area under the curve (AUC) results consistently demonstrated superior classification performance for task-based EEG compared to resting-state EEG across all group distinctions. No significant performance difference was observed between the two DL models. Conclusions: We proposed a cognitive task-based approach for early AD spectrum diagnosis via EEG, offering greater accuracy by leveraging advanced DL models.<\/jats:p>","DOI":"10.3390\/diagnostics15040448","type":"journal-article","created":{"date-parts":[[2025,2,12]],"date-time":"2025-02-12T10:25:57Z","timestamp":1739355957000},"page":"448","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Early Diagnosis of Alzheimer\u2019s Disease in Human Participants Using EEGConformer and Attention-Based LSTM During the Short Question Task"],"prefix":"10.3390","volume":"15","author":[{"given":"Seul-Kee","family":"Kim","sequence":"first","affiliation":[{"name":"Bionics Research Center, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea"},{"name":"Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8013-9349","authenticated-orcid":false,"given":"Jung Bin","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Neurology, Korea University Anam Hospital, Korea University College of Medicine, Seoul 02841, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9991-3664","authenticated-orcid":false,"given":"Hayom","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Neurology, Korea University Anam Hospital, Korea University College of Medicine, Seoul 02841, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5769-1039","authenticated-orcid":false,"given":"Laehyun","family":"Kim","sequence":"additional","affiliation":[{"name":"Bionics Research Center, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea"},{"name":"Department of HY-KIST Bio-Convergence, Hanyang University, Seoul 04763, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sang Hee","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Alzheimer\u2019s Association (2021). 2021 Alzheimer\u2019s disease facts and figures. Alzheimers Dement., 17, 327\u2013406.","DOI":"10.1002\/alz.12328"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Gasmi, K., Alyami, A., Hamid, O., Altaieb, M.O., Shahin, O.R., Ben Ammar, L., and Shehab, A. (2024). Optimized Hybrid Deep Learning Framework for Early Detection of Alzheimer\u2019s Disease Using Adaptive Weight Selection. Diagnostics, 14.","DOI":"10.3390\/diagnostics14242779"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1056\/NEJMoa1202753","article-title":"Clinical and biomarker changes in dominantly inherited Alzheimer\u2019s disease","volume":"367","author":"Bateman","year":"2012","journal-title":"N. Engl. J. Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1212\/WNL.0b013e3181d3e3e9","article-title":"Longitudinal cognitive decline is associated with fibrillar amyloid-beta measured by [11C]PiB","volume":"74","author":"Resnick","year":"2010","journal-title":"Neurology"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1016\/j.jalz.2018.02.018","article-title":"NIA-AA research framework: Toward a biological definition of Alzheimer\u2019s disease","volume":"14","author":"Jack","year":"2018","journal-title":"Alzheimers Dement."},{"key":"ref_6","unstructured":"Prince, M., Comas-Herrera, A., Knapp, M., Guerchet, M., and Karagiannidou, M. (2025, February 09). Improving Healthcare for People Living with Dementia: Coverage, Quality, and Costs Now and in the Future. World Alzheimer Report. Available online: https:\/\/www.alzint.org\/u\/WorldAlzheimerReport2016.pdf."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1016\/j.jalz.2011.03.008","article-title":"The diagnosis of mild cognitive impairment due to Alzheimer\u2019s disease: Recommendations from the National Institute on Aging-Alzheimer\u2019s Association workgroups on diagnostic guidelines for Alzheimer\u2019s disease","volume":"7","author":"Albert","year":"2011","journal-title":"Alzheimers Dement."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1016\/j.jalz.2014.01.001","article-title":"A conceptual framework for research on subjective cognitive decline in preclinical Alzheimer\u2019s disease","volume":"10","author":"Jessen","year":"2014","journal-title":"Alzheimers Dement."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"046013","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":"ref_10","doi-asserted-by":"crossref","unstructured":"Huang, W., Li, X., Li, X., Kang, G., Han, Y., and Shu, N. (2021). Combined support vector machine classifier and brain structural network features for the individual classification of amnestic mild cognitive impairment and subjective cognitive decline patients. Front. Aging Neurosci., 13.","DOI":"10.3389\/fnagi.2021.687927"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Lazarou, I., Georgiadis, K., Nikolopoulos, S., Oikonomou, V.P., Tsolaki, A., Kompatsiaris, I., Tsolaki, M., and Kugiumtzis, D. (2020). A novel connectome-based electrophysiological study of subjective cognitive decline related to Alzheimer\u2019s disease by using resting-state high-density EEG EGI GES 300. Brain Sci., 10.","DOI":"10.3390\/brainsci10060392"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1159\/000508392","article-title":"The power of EEG to predict conversion from mild cognitive impairment and subjective cognitive decline to dementia","volume":"49","author":"Engedal","year":"2020","journal-title":"Dement. Geriatr. Cogn. Disord."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1287","DOI":"10.1016\/j.clinph.2020.03.003","article-title":"Early diagnosis of Alzheimer\u2019s disease: The role of biomarkers including advanced EEG signal analysis. Report from the IFCN-sponsored panel of experts","volume":"131","author":"Rossini","year":"2020","journal-title":"Clin. Neurophysiol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"927573","DOI":"10.4061\/2011\/927573","article-title":"Electroencephalographic rhythms in Alzheimer\u2019s disease","volume":"2011","author":"Lizio","year":"2011","journal-title":"Int. J. Alzheimers Dis."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"71840","DOI":"10.1109\/ACCESS.2023.3294618","article-title":"DICE-net: A novel convolution-transformer architecture for Alzheimer detection in EEG signals","volume":"11","author":"Miltiadous","year":"2023","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Dubey, Y., Bhongade, A., Palsodkar, P., and Fulzele, P. (2024). Efficient Explainable Models for Alzheimer\u2019s Disease Classification with Feature Selection and Data Balancing Approach Using Ensemble Learning. Diagnostics, 14.","DOI":"10.3390\/diagnostics14242770"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Alshamlan, H., Alwassel, A., Banafa, A., and Alsaleem, L. (2024). Improving Alzheimer\u2019s Disease Prediction with Different Machine Learning Approaches and Feature Selection Techniques. Diagnostics, 14.","DOI":"10.3390\/diagnostics14192237"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1109\/TETCI.2022.3186180","article-title":"A long short-term memory-based framework for early detection of mild cognitive impairment from EEG signals","volume":"7","author":"Alvi","year":"2022","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Kumar Ravikanti, D., and Saravanan, S. (2023). EEGAlzheimer\u2019sNet: Development of transformer-based attention long short-term memory network for detecting Alzheimer\u2019s disease using EEG signal. Biomed. Signal Process. Control, 86.","DOI":"10.1016\/j.bspc.2023.105318"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Imani, M. (2023). Alzheimer\u2019s disease diagnosis using fusion of high informative BiLSTM and CNN features of EEG signal. Biomed. Signal Process. Control, 86.","DOI":"10.1016\/j.bspc.2023.105298"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Lee, H.T., Cheon, H.R., Lee, S.H., Shim, M., and Hwang, H.J. (2023). Risk of data leakage in estimating the diagnostic performance of a deep-learning-based computer-aided system for psychiatric disorders. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-43542-8"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"e057573","DOI":"10.1002\/alz.057573","article-title":"DeepADNet: A CNN-LSTM model for the multi-class classification of Alzheimer\u2019s disease using multichannel EEG","volume":"17","author":"Ho","year":"2021","journal-title":"Alzheimers Dement."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1273","DOI":"10.1007\/s12559-022-10075-7","article-title":"Spatiotemporal EEG dynamics of prospective memory in ageing and mild cognitive impairment","volume":"15","author":"Howard","year":"2023","journal-title":"Cognit. Comput."},{"key":"ref_24","unstructured":"Kang, Y., Na, D.L., and Hahn, S.J. (2003). Seoul Neuropsychological Screening Battery, Human Brain Research & Consulting, Co."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2412","DOI":"10.1212\/WNL.43.11.2412-a","article-title":"The clinical dementia rating (CDR): Current version and scoring rules","volume":"43","author":"Morris","year":"1993","journal-title":"Neurology"},{"key":"ref_26","first-page":"746","article-title":"Validation of the new interpretive guidelines for the clinical dementia rating scale sum of boxes score in the national Alzheimer\u2019s coordinating center database","volume":"67","author":"Lacritz","year":"2010","journal-title":"Arch. Neurol."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Kim, S.K., Kim, H., Kim, S.H., Kim, J.B., and Kim, L. (2024). Electroencephalography-based classification of Alzheimer\u2019s disease spectrum during computer-based cognitive testing. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-55656-8"},{"key":"ref_28","unstructured":"Su, J., Duh, K., and Carreras, X. (2016, January 1\u20135). Attention-based LSTM for aspect-level sentiment classification. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, Austin, TX, USA."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3113","DOI":"10.1109\/JSEN.2019.2956998","article-title":"Classification of hand movements from EEG using a deep attention-based LSTM network","volume":"20","author":"Zhang","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Kim, Y., and Choi, A. (2020). EEG-based emotion classification using long short-term memory network with attention mechanism. Sensors, 20.","DOI":"10.3390\/s20236727"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"710","DOI":"10.1109\/TNSRE.2022.3230250","article-title":"EEG conformer: Convolutional transformer for EEG decoding and visualization","volume":"31","author":"Song","year":"2023","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"120054","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"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/4\/448\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:32:22Z","timestamp":1760027542000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/4\/448"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,12]]},"references-count":32,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,2]]}},"alternative-id":["diagnostics15040448"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics15040448","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,12]]}}}