{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,7]],"date-time":"2026-11-07T04:00:14Z","timestamp":1794024014201,"version":"4.8.0"},"reference-count":65,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T00:00:00Z","timestamp":1751414400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T00:00:00Z","timestamp":1751414400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["No.LGF21F020024"],"award-info":[{"award-number":["No.LGF21F020024"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-025-06519-3","type":"journal-article","created":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T04:48:58Z","timestamp":1751431738000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["A dual path graph neural network framework for dementia diagnosis"],"prefix":"10.1038","volume":"15","author":[{"given":"Denghui","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenxuan","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,2]]},"reference":[{"key":"6519_CR1","doi-asserted-by":"publisher","first-page":"116795","DOI":"10.1016\/j.neuroimage.2020.116795","volume":"215","author":"FR Farina","year":"2020","unstructured":"Farina, F. R. et al. A comparison of resting state EEG and structural MRI for classifying Alzheimer\u2019s disease and mild cognitive impairment. Neuroimage 215, 116795 (2020).","journal-title":"Neuroimage"},{"key":"6519_CR2","doi-asserted-by":"publisher","first-page":"105242","DOI":"10.1016\/j.cmpb.2019.105242","volume":"187","author":"MA Ebrahimighahnavieh","year":"2020","unstructured":"Ebrahimighahnavieh, M. A., Luo, S. & Chiong, R. Deep learning to detect Alzheimer\u2019s disease from neuroimaging: A systematic literature review. Comput. Methods Prog. Biomed. 187, 105242 (2020).","journal-title":"Comput. Methods Prog. Biomed."},{"key":"6519_CR3","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1016\/j.tins.2017.02.004","volume":"40","author":"MX Cohen","year":"2017","unstructured":"Cohen, M. X. Where does EEG come from and what does it mean?. Trends Neurosci. 40, 208\u2013218 (2017).","journal-title":"Trends Neurosci."},{"key":"6519_CR4","doi-asserted-by":"publisher","first-page":"860","DOI":"10.1002\/hbm.25683","volume":"43","author":"J Cao","year":"2022","unstructured":"Cao, J. et al. Brain functional and effective connectivity based on electroencephalography recordings: A review. Hum. Brain Mapp. 43, 860\u2013879 (2022).","journal-title":"Hum. Brain Mapp."},{"key":"6519_CR5","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1016\/j.dcn.2015.01.011","volume":"12","author":"D Goldenberg","year":"2015","unstructured":"Goldenberg, D. & Galv\u00e1n, A. The use of functional and effective connectivity techniques to understand the developing brain. Dev. Cognit. Neurosci. 12, 155\u2013164 (2015).","journal-title":"Dev. Cognit. Neurosci."},{"key":"6519_CR6","doi-asserted-by":"crossref","unstructured":"Zhang, K., Ye, Z., Ai, Q., Xie, X. & Liu, Y. Gnn4eeg: A benchmark and toolkit for electroencephalography classification with graph neural network. In Companion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing. 612\u2013617 (2024).","DOI":"10.1145\/3675094.3678475"},{"key":"6519_CR7","doi-asserted-by":"publisher","first-page":"668","DOI":"10.1016\/j.neuroimage.2009.06.056","volume":"49","author":"J Dauwels","year":"2010","unstructured":"Dauwels, J., Vialatte, F., Musha, T. & Cichocki, A. A comparative study of synchrony measures for the early diagnosis of Alzheimer\u2019s disease based on EEG. NeuroImage 49, 668\u2013693 (2010).","journal-title":"NeuroImage"},{"key":"6519_CR8","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1007\/s11062-015-9496-z","volume":"47","author":"O Vy\u0161ata","year":"2015","unstructured":"Vy\u0161ata, O., Vali\u0161, M., Proch\u00e1zka, A., Rusina, R. & Pazdera, L. Linear and nonlinear EEG synchronization in Alzheimer\u2019s disease. Neurophysiology 47, 46\u201352 (2015).","journal-title":"Neurophysiology"},{"key":"6519_CR9","doi-asserted-by":"publisher","first-page":"e1000100","DOI":"10.1371\/journal.pcbi.1000100","volume":"4","author":"K Supekar","year":"2008","unstructured":"Supekar, K., Menon, V., Rubin, D., Musen, M. & Greicius, M. D. Network analysis of intrinsic functional brain connectivity in Alzheimer\u2019s disease. PLoS Comput. Biol. 4, e1000100 (2008).","journal-title":"PLoS Comput. Biol."},{"key":"6519_CR10","doi-asserted-by":"publisher","first-page":"992","DOI":"10.1109\/JBHI.2021.3105397","volume":"26","author":"D Klepl","year":"2021","unstructured":"Klepl, D. et al. Characterising Alzheimer\u2019s disease with EEG-based energy landscape analysis. IEEE J. Biomed. Health Inform. 26, 992\u20131000 (2021).","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"6519_CR11","first-page":"1324","volume":"2021","author":"Z Jia","year":"2020","unstructured":"Jia, Z. et al. Graphsleepnet: Adaptive spatial-temporal graph convolutional networks for sleep stage classification. In Ijcai 2021, 1324\u20131330 (2020).","journal-title":"In Ijcai"},{"key":"6519_CR12","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1109\/TAFFC.2018.2817622","volume":"11","author":"T Song","year":"2018","unstructured":"Song, T., Zheng, W., Song, P. & Cui, Z. EEG emotion recognition using dynamical graph convolutional neural networks. IEEE Trans. Affect. Comput. 11, 532\u2013541 (2018).","journal-title":"IEEE Trans. Affect. Comput."},{"key":"6519_CR13","doi-asserted-by":"publisher","first-page":"1290","DOI":"10.1109\/TAFFC.2020.2994159","volume":"13","author":"P Zhong","year":"2020","unstructured":"Zhong, P., Wang, D. & Miao, C. EEG-based emotion recognition using regularized graph neural networks. IEEE Trans. Affect. Comput. 13, 1290\u20131301 (2020).","journal-title":"IEEE Trans. Affect. Comput."},{"key":"6519_CR14","doi-asserted-by":"publisher","first-page":"28400","DOI":"10.1038\/s41598-024-79981-0","volume":"14","author":"T Xing","year":"2024","unstructured":"Xing, T. et al. An adaptive multi-graph neural network with multimodal feature fusion learning for MDD detection. Sci. Rep. 14, 28400 (2024).","journal-title":"Sci. Rep."},{"key":"6519_CR15","doi-asserted-by":"publisher","first-page":"10282","DOI":"10.1038\/s41598-022-14217-7","volume":"12","author":"S Asadzadeh","year":"2022","unstructured":"Asadzadeh, S., Yousefi Rezaii, T., Beheshti, S. & Meshgini, S. Accurate emotion recognition using Bayesian model based EEG sources as dynamic graph convolutional neural network nodes. Sci. Rep. 12, 10282 (2022).","journal-title":"Sci. Rep."},{"key":"6519_CR16","doi-asserted-by":"publisher","first-page":"5194","DOI":"10.1002\/hbm.25994","volume":"43","author":"X Shan","year":"2022","unstructured":"Shan, X. et al. Spatial-temporal graph convolutional network for Alzheimer classification based on brain functional connectivity imaging of electroencephalogram. Hum. Brain Mapp. 43, 5194\u20135209 (2022).","journal-title":"Hum. Brain Mapp."},{"key":"6519_CR17","doi-asserted-by":"crossref","unstructured":"Radwan, M., Lind, P.\u00a0G. & Yazidi, A. An interpretable graph based model for classification of EEG using directional functional connectivity. In 2024 IEEE International Symposium on Biomedical Imaging (ISBI). 1\u20135 (IEEE, 2024).","DOI":"10.1109\/ISBI56570.2024.10635115"},{"key":"6519_CR18","doi-asserted-by":"publisher","first-page":"107701","DOI":"10.1016\/j.compbiomed.2023.107701","volume":"168","author":"J Cao","year":"2024","unstructured":"Cao, J., Yang, L., Sarrigiannis, P. G., Blackburn, D. & Zhao, Y. Dementia classification using a graph neural network on imaging of effective brain connectivity. Comput. Biol. Med. 168, 107701 (2024).","journal-title":"Comput. Biol. Med."},{"key":"6519_CR19","doi-asserted-by":"publisher","first-page":"2651","DOI":"10.1109\/TNSRE.2022.3204913","volume":"30","author":"D Klepl","year":"2022","unstructured":"Klepl, D., He, F., Wu, M., Blackburn, D. J. & Sarrigiannis, P. EEG-based graph neural network classification of Alzheimer\u2019s disease: An empirical evaluation of functional connectivity methods. IEEE Trans. Neural Syst. Rehabil. Eng. 30, 2651\u20132660 (2022).","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"6519_CR20","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1186\/s12868-024-00877-w","volume":"25","author":"S Wu","year":"2024","unstructured":"Wu, S. et al. Changes of brain functional network in Alzheimer\u2019s disease and frontotemporal dementia: A graph-theoretic analysis. BMC Neurosci. 25, 30 (2024).","journal-title":"BMC Neurosci."},{"key":"6519_CR21","doi-asserted-by":"publisher","first-page":"3978","DOI":"10.1109\/TNSRE.2023.3321634","volume":"31","author":"D Klepl","year":"2023","unstructured":"Klepl, D., He, F., Wu, M., Blackburn, D. J. & Sarrigiannis, P. Adaptive gated graph convolutional network for explainable diagnosis of Alzheimer\u2019s disease using EEG data. IEEE Trans. Neural Syst. Rehabil. Eng. 31, 3978\u20133987 (2023).","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"6519_CR22","doi-asserted-by":"publisher","first-page":"1272834","DOI":"10.3389\/fnins.2023.1272834","volume":"17","author":"Y Chen","year":"2023","unstructured":"Chen, Y., Wang, H., Zhang, D., Zhang, L. & Tao, L. Multi-feature fusion learning for Alzheimer\u2019s disease prediction using EEG signals in resting state. Front. Neurosci. 17, 1272834 (2023).","journal-title":"Front. Neurosci."},{"key":"6519_CR23","doi-asserted-by":"publisher","first-page":"68","DOI":"10.3390\/brainsci15010068","volume":"15","author":"A Sanati Fahandari","year":"2025","unstructured":"Sanati Fahandari, A., Moshiryan, S. & Goshvarpour, A. Diagnosis of cognitive and mental disorders: A new approach based on spectral-spatiotemporal analysis and local graph structures of electroencephalogram signals. Brain Sci. 15, 68 (2025).","journal-title":"Brain Sci."},{"key":"6519_CR24","doi-asserted-by":"crossref","unstructured":"Ieracitano, C. et al. A time-frequency based machine learning system for brain states classification via EEG signal processing. In 2019 International Joint Conference on Neural Networks (IJCNN). 1\u20138 (IEEE, 2019).","DOI":"10.1109\/IJCNN.2019.8852240"},{"key":"6519_CR25","doi-asserted-by":"publisher","first-page":"5413","DOI":"10.3390\/app12115413","volume":"12","author":"D Pirrone","year":"2022","unstructured":"Pirrone, D., Weitschek, E., Di Paolo, P., De Salvo, S. & De Cola, M. C. EEG signal processing and supervised machine learning to early diagnose Alzheimer\u2019s disease. Appl. Sci. 12, 5413 (2022).","journal-title":"Appl. Sci."},{"key":"6519_CR26","doi-asserted-by":"crossref","unstructured":"Tavares, G., San-Martin, R., Ianof, J.\u00a0N., Anghinah, R. & Fraga, F.\u00a0J. Improvement in the automatic classification of Alzheimer\u2019s disease using EEG after feature selection. In 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC). 1264\u20131269 (IEEE, 2019).","DOI":"10.1109\/SMC.2019.8914006"},{"key":"6519_CR27","doi-asserted-by":"crossref","unstructured":"Fiscon, G., Weitschek, E., De Cola, M.\u00a0C., Felici, G. & Bertolazzi, P. An integrated approach based on EEG signals processing combined with supervised methods to classify Alzheimer\u2019s disease patients. In 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 2750\u20132752 (IEEE, 2018).","DOI":"10.1109\/BIBM.2018.8621473"},{"key":"6519_CR28","doi-asserted-by":"publisher","first-page":"24365","DOI":"10.1038\/s41598-024-74035-x","volume":"14","author":"G Mostile","year":"2024","unstructured":"Mostile, G. et al. Differentiating neurodegenerative diseases based on EEG complexity. Sci. Rep. 14, 24365 (2024).","journal-title":"Sci. Rep."},{"key":"6519_CR29","doi-asserted-by":"publisher","first-page":"698386","DOI":"10.3389\/fncom.2021.698386","volume":"15","author":"N Youssef","year":"2021","unstructured":"Youssef, N. et al. Functional brain networks in mild cognitive impairment based on resting electroencephalography signals. Front. Comput. Neurosci. 15, 698386 (2021).","journal-title":"Front. Comput. Neurosci."},{"key":"6519_CR30","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.neucom.2018.09.071","volume":"323","author":"C Ieracitano","year":"2019","unstructured":"Ieracitano, C., Mammone, N., Bramanti, A., Hussain, A. & Morabito, F. C. A convolutional neural network approach for classification of dementia stages based on 2d-spectral representation of eeg recordings. Neurocomputing 323, 96\u2013107 (2019).","journal-title":"Neurocomputing"},{"key":"6519_CR31","doi-asserted-by":"publisher","first-page":"103049","DOI":"10.1016\/j.bspc.2021.103049","volume":"70","author":"EM Rad","year":"2021","unstructured":"Rad, E. M., Azarnoosh, M., Ghoshuni, M. & Khalilzadeh, M. M. Diagnosis of mild Alzheimer\u2019s disease by EEG and ERP signals using linear and nonlinear classifiers. Biomed. Signal Process. Control 70, 103049 (2021).","journal-title":"Biomed. Signal Process. Control"},{"key":"6519_CR32","doi-asserted-by":"publisher","first-page":"110882","DOI":"10.1016\/j.patcog.2024.110882","volume":"157","author":"R Sheikhpour","year":"2025","unstructured":"Sheikhpour, R., Berahmand, K., Mohammadi, M. & Khosravi, H. Sparse feature selection using hypergraph Laplacian-based semi-supervised discriminant analysis. Pattern Recognit. 157, 110882 (2025).","journal-title":"Pattern Recognit."},{"key":"6519_CR33","doi-asserted-by":"crossref","unstructured":"Sharifi, S. Enhancing kidney transplantation through multi-agent kidney exchange programs: A comprehensive review and optimization models. arXiv preprint arXiv:2502.07819 (2025).","DOI":"10.5267\/j.ijiec.2024.12.002"},{"key":"6519_CR34","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1007\/s10462-023-10662-6","volume":"57","author":"K Berahmand","year":"2024","unstructured":"Berahmand, K., Daneshfar, F., Salehi, E. S., Li, Y. & Xu, Y. Autoencoders and their applications in machine learning: A survey. Artif. Intell. Rev. 57, 28 (2024).","journal-title":"Artif. Intell. Rev."},{"key":"6519_CR35","unstructured":"Jiang, W.-B., Zhao, L.-M. & Lu, B.-L. Large brain model for learning generic representations with tremendous EEG data in BCI. arXiv preprint arXiv:2405.18765 (2024)."},{"key":"6519_CR36","unstructured":"Jiang, W.-B., Zhao, L.-M. & Lu, B.-L. Large brain model for learning generic representations with tremendous EEG data in BCI. arXiv preprint arXiv:2405.18765 (2024)."},{"key":"6519_CR37","doi-asserted-by":"publisher","first-page":"8184","DOI":"10.1038\/s41598-023-32664-8","volume":"13","author":"ML Vicchietti","year":"2023","unstructured":"Vicchietti, M. L., Ramos, F. M., Betting, L. E. & Campanharo, A. S. Computational methods of EEG signals analysis for Alzheimer\u2019s disease classification. Sci. Rep. 13, 8184 (2023).","journal-title":"Sci. Rep."},{"key":"6519_CR38","doi-asserted-by":"publisher","first-page":"26002","DOI":"10.1038\/s41598-024-77876-8","volume":"14","author":"M Khosravi","year":"2024","unstructured":"Khosravi, M., Parsaei, H., Rezaee, K. & Helfroush, M. S. Fusing convolutional learning and attention-based bi-LSTM networks for early Alzheimer\u2019s diagnosis from EEG signals towards IOMT. Sci. Rep. 14, 26002 (2024).","journal-title":"Sci. Rep."},{"key":"6519_CR39","unstructured":"Dosovitskiy, A. et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)."},{"key":"6519_CR40","unstructured":"Vaswani, A. Attention is all you need. Advances in Neural Information Processing Systems (2017)."},{"key":"6519_CR41","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1109\/TII.2022.3161674","volume":"19","author":"X Zhao","year":"2022","unstructured":"Zhao, X. et al. Multiscale deep graph convolutional networks for intelligent fault diagnosis of rotor-bearing system under fluctuating working conditions. IEEE Trans. Indus. Inform. 19, 166\u2013176 (2022).","journal-title":"IEEE Trans. Indus. Inform."},{"key":"6519_CR42","unstructured":"Liao, R., Zhao, Z., Urtasun, R. & Zemel, R.\u00a0S. Lanczosnet: Multi-scale deep graph convolutional networks. arXiv preprint arXiv:1901.01484 (2019)."},{"key":"6519_CR43","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1109\/TAI.2023.3241896","volume":"5","author":"Y Wang","year":"2023","unstructured":"Wang, Y., Wu, M., Li, X., Xie, L. & Chen, Z. Multivariate time-series representation learning via hierarchical correlation pooling boosted graph neural network. IEEE Trans. Artif. Intell. 5, 321\u2013333 (2023).","journal-title":"IEEE Trans. Artif. Intell."},{"key":"6519_CR44","doi-asserted-by":"crossref","unstructured":"Hu, J., Chen, S., Pan, Z., Zeng, S. & Yang, W. Perspective+ unet: Enhancing segmentation with bi-path fusion and efficient non-local attention for superior receptive fields. In International Conference on Medical Image Computing and Computer-Assisted Intervention. 499\u2013509 (Springer, 2024).","DOI":"10.1007\/978-3-031-72114-4_48"},{"key":"6519_CR45","doi-asserted-by":"publisher","first-page":"112537","DOI":"10.1016\/j.measurement.2023.112537","volume":"209","author":"Z Chen","year":"2023","unstructured":"Chen, Z., Yang, J., Feng, Z., Chen, L. & Li, L. Bishufflenext: A lightweight bi-path network for remote sensing scene classification. Measurement 209, 112537 (2023).","journal-title":"Measurement"},{"key":"6519_CR46","doi-asserted-by":"crossref","unstructured":"Tsai, Y.-H.\u00a0H. et al. Multimodal transformer for unaligned multimodal language sequences. In Proceedings of the Conference. Association for Computational Linguistics. Meeting. Vol. 2019. 6558 (NIH Public Access, 2019).","DOI":"10.18653\/v1\/P19-1656"},{"key":"6519_CR47","doi-asserted-by":"publisher","first-page":"95","DOI":"10.3390\/data8060095","volume":"8","author":"A Miltiadous","year":"2023","unstructured":"Miltiadous, A. et al. A dataset of scalp EEG recordings of Alzheimer\u2019s disease, frontotemporal dementia and healthy subjects from routine EEG. Data 8, 95 (2023).","journal-title":"Data"},{"key":"6519_CR48","doi-asserted-by":"publisher","first-page":"120054","DOI":"10.1016\/j.neuroimage.2023.120054","volume":"272","author":"M-J Kim","year":"2023","unstructured":"Kim, M.-J., Youn, Y. C. & Paik, J. Deep learning-based EEG analysis to classify normal, mild cognitive impairment, and dementia: Algorithms and dataset. NeuroImage 272, 120054 (2023).","journal-title":"NeuroImage"},{"key":"6519_CR49","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1038\/323533a0","volume":"323","author":"DE Rumelhart","year":"1986","unstructured":"Rumelhart, D. E., Hinton, G. E. & Williams, R. J. Learning representations by back-propagating errors. Nature 323, 533\u2013536 (1986).","journal-title":"Nature"},{"key":"6519_CR50","doi-asserted-by":"crossref","unstructured":"Boser, B.\u00a0E. Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory. Vol. 144 (1992).","DOI":"10.1145\/130385.130401"},{"key":"6519_CR51","doi-asserted-by":"publisher","first-page":"710","DOI":"10.1109\/TNSRE.2022.3230250","volume":"31","author":"Y Song","year":"2022","unstructured":"Song, Y., Zheng, Q., Liu, B. & Gao, X. EEG conformer: Convolutional transformer for EEG decoding and visualization. IEEE Trans. Neural Syst. Rehabil. Eng. 31, 710\u2013719 (2022).","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"6519_CR52","doi-asserted-by":"publisher","first-page":"382","DOI":"10.1109\/TAFFC.2020.3025777","volume":"14","author":"W Tao","year":"2020","unstructured":"Tao, W. et al. EEG-based emotion recognition via channel-wise attention and self attention. IEEE Trans. Affect. Comput. 14, 382\u2013393 (2020).","journal-title":"IEEE Trans. Affect. Comput."},{"key":"6519_CR53","unstructured":"Xing, Y., Wang, X., Li, Y., Huang, H. & Shi, C. Less is more: On the over-globalizing problem in graph transformers. arXiv preprint arXiv:2405.01102 (2024)."},{"key":"6519_CR54","unstructured":"Kipf, T.\u00a0N. & Welling, M. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)."},{"key":"6519_CR55","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1109\/TAFFC.2018.2817622","volume":"11","author":"T Song","year":"2018","unstructured":"Song, T., Zheng, W., Song, P. & Cui, Z. EEG emotion recognition using dynamical graph convolutional neural networks. IEEE Trans. Affect. Comput. 11, 532\u2013541 (2018).","journal-title":"IEEE Trans. Affect. Comput."},{"key":"6519_CR56","doi-asserted-by":"crossref","unstructured":"Miltiadous, A., Gionanidis, E., Tzimourta, K.\u00a0D., Giannakeas, N. & Tzallas, A.\u00a0T. Dice-net: a novel convolution-transformer architecture for Alzheimer detection in EEG signals. IEEE Access (2023).","DOI":"10.1109\/ACCESS.2023.3294618"},{"key":"6519_CR57","first-page":"43","volume":"14","author":"A Parihar","year":"2024","unstructured":"Parihar, A. et al. Analysis of EEG signals with the use of wavelet transform for accurate classification of Alzheimer disease, frontotemporal dementia and healthy subjects using machine learning models. Full Length Article 14, 43\u20133 (2024).","journal-title":"Full Length Article"},{"key":"6519_CR58","doi-asserted-by":"publisher","first-page":"1437","DOI":"10.3390\/diagnostics11081437","volume":"11","author":"A Miltiadous","year":"2021","unstructured":"Miltiadous, A. et al. Alzheimer\u2019s disease and frontotemporal dementia: A robust classification method of EEG signals and a comparison of validation methods. Diagnostics 11, 1437 (2021).","journal-title":"Diagnostics"},{"key":"6519_CR59","first-page":"1324","volume":"2021","author":"Z Jia","year":"2020","unstructured":"Jia, Z. et al. Graphsleepnet: Adaptive spatial-temporal graph convolutional networks for sleep stage classification. In Ijcai 2021, 1324\u20131330 (2020).","journal-title":"In Ijcai"},{"key":"6519_CR60","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1136\/jnnp.71.4.441","volume":"71","author":"A Du","year":"2001","unstructured":"Du, A. et al. Magnetic resonance imaging of the entorhinal cortex and hippocampus in mild cognitive impairment and Alzheimer\u2019s disease. J. Neurol. Neurosurg. Psychiatry 71, 441\u2013447 (2001).","journal-title":"J. Neurol. Neurosurg. Psychiatry"},{"key":"6519_CR61","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1016\/j.jalz.2018.02.018","volume":"14","author":"CR Jack Jr","year":"2018","unstructured":"Jack, C. R. Jr. et al. Nia-aa research framework: Toward a biological definition of Alzheimer\u2019s disease. Alzheimer\u2019s Dementia 14, 535\u2013562 (2018).","journal-title":"Alzheimer\u2019s Dementia"},{"key":"6519_CR62","doi-asserted-by":"publisher","first-page":"1490","DOI":"10.1016\/j.clinph.2004.01.001","volume":"115","author":"J Jeong","year":"2004","unstructured":"Jeong, J. EEG dynamics in patients with Alzheimer\u2019s disease. Clin. Neurophysiol. 115, 1490\u20131505 (2004).","journal-title":"Clin. Neurophysiol."},{"key":"6519_CR63","doi-asserted-by":"publisher","first-page":"e28071","DOI":"10.1371\/journal.pone.0028071","volume":"6","author":"K El Emam","year":"2011","unstructured":"El Emam, K., Jonker, E., Arbuckle, L. & Malin, B. A systematic review of re-identification attacks on health data. PloS one 6, e28071 (2011).","journal-title":"PloS one"},{"key":"6519_CR64","unstructured":"W.\u00a0H. Organization et al. Ethics and governance of artificial intelligence for health: Who guidance: Executive summary. In Ethics and Governance of Artificial Intelligence for Health: WHO Guidance: Executive Summary (2021)."},{"key":"6519_CR65","doi-asserted-by":"publisher","first-page":"772","DOI":"10.1001\/jama.2020.12134","volume":"324","author":"S Palmqvist","year":"2020","unstructured":"Palmqvist, S. et al. Discriminative accuracy of plasma phospho-tau217 for Alzheimer disease vs other neurodegenerative disorders. Jama 324, 772\u2013781 (2020).","journal-title":"Jama"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-06519-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-06519-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-06519-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T06:18:13Z","timestamp":1751437093000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-06519-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,2]]},"references-count":65,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["6519"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-06519-3","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,2]]},"assertion":[{"value":"18 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 June 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 July 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"23319"}}