{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T22:57:45Z","timestamp":1781132265146,"version":"3.54.1"},"publisher-location":"Singapore","reference-count":26,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819500321","type":"print"},{"value":"9789819500338","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-95-0033-8_39","type":"book-chapter","created":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T08:00:55Z","timestamp":1753344055000},"page":"468-480","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multidimensional EEG Signal Analysis and Vision Transformer-Masked Autoencoder-Based Image Processing for Alzheimer\u2019s Disease Detection"],"prefix":"10.1007","author":[{"given":"Shu","family":"Xiang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haobo","family":"Ling","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shiwei","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meihong","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,25]]},"reference":[{"issue":"24","key":"39_CR1","doi-asserted-by":"publisher","first-page":"5789","DOI":"10.3390\/molecules25245789","volume":"25","author":"Z Breijyeh","year":"2020","unstructured":"Breijyeh, Z., Karaman, R.: Comprehensive review on Alzheimer\u2019s disease: causes and treatment. Molecules 25(24), 5789 (2020)","journal-title":"Molecules"},{"issue":"5","key":"39_CR2","doi-asserted-by":"publisher","first-page":"1218","DOI":"10.3390\/electronics12051218","volume":"12","author":"M Odusami","year":"2023","unstructured":"Odusami, M., Maskeli\u016bnas, R., Dama\u0161evi\u010dius, R.: Pixel-level fusion approach with vision transformer for early detection of Alzheimer\u2019s disease. Electronics 12(5), 1218 (2023)","journal-title":"Electronics"},{"issue":"6","key":"39_CR3","doi-asserted-by":"publisher","first-page":"893","DOI":"10.3390\/brainsci13060893","volume":"13","author":"A Chelladurai","year":"2023","unstructured":"Chelladurai, A., Narayan, D.L., Divakarachari, P.B., Loganathan, U.: FMRI-based Alzheimer\u2019s disease detection using the SAS method with multi-layer perceptron network. Brain Sci. 13(6), 893 (2023)","journal-title":"Brain Sci."},{"key":"39_CR4","doi-asserted-by":"publisher","first-page":"113274","DOI":"10.1016\/j.measurement.2023.113274","volume":"209","author":"A Modir","year":"2023","unstructured":"Modir, A., Shamekhi, S., Ghaderyan, P.: A systematic review and methodological analysis of EEG-based biomarkers of Alzheimer\u2019s disease. Measurement 209, 113274 (2023)","journal-title":"Measurement"},{"issue":"1","key":"39_CR5","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1186\/s13195-024-01582-w","volume":"16","author":"CA Chetty","year":"2024","unstructured":"Chetty, C.A., et al.: EEG biomarkers in Alzheimer\u2019s and prodromal Alzheimer\u2019s: a comprehensive analysis of spectral and connectivity features. Alz. Res. Ther. 16(1), 236 (2024)","journal-title":"Alz. Res. Ther."},{"issue":"3","key":"39_CR6","doi-asserted-by":"publisher","first-page":"031001","DOI":"10.1088\/1741-2552\/ab0ab5","volume":"16","author":"A Craik","year":"2019","unstructured":"Craik, A., He, Y., Contreras-Vidal, J.L.: Deep learning for electroencephalogram (EEG) classification tasks: a review. J. Neural Eng. 16(3), 031001 (2019)","journal-title":"J. Neural Eng."},{"key":"39_CR7","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D. et al.: An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ArXiv abs\/2010.11929 (2020)"},{"key":"39_CR8","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1016\/j.inffus.2017.10.006","volume":"42","author":"Q Zhang","year":"2018","unstructured":"Zhang, Q., Yang, L.T., Chen, Z., Li, P.: A survey on deep learning for big data. Inf. Fusion 42, 146\u2013157 (2018)","journal-title":"Inf. Fusion"},{"issue":"6","key":"39_CR9","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(6), 95 (2023)","journal-title":"Data"},{"key":"39_CR10","doi-asserted-by":"publisher","first-page":"101878","DOI":"10.1016\/j.pneurobio.2020.101878","volume":"194","author":"Z Jafari","year":"2020","unstructured":"Jafari, Z., Kolb, B.E., Mohajerani, M.H.: Neural oscillations and brain stimulation in Alzheimer\u2019s disease. Prog. Neurobiol. 194, 101878 (2020)","journal-title":"Prog. Neurobiol."},{"issue":"1","key":"39_CR11","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1016\/j.dsp.2007.12.004","volume":"19","author":"E Sejdic","year":"2009","unstructured":"Sejdic, E., Djurovi\u0107, I., Jiang, J.: Time\u2013frequency feature representation using energy concentration: an overview of recent advances. Digit. Signal Process. 19(1), 153\u2013183 (2009)","journal-title":"Digit. Signal Process."},{"key":"39_CR12","doi-asserted-by":"publisher","first-page":"101114","DOI":"10.1016\/j.dcn.2022.101114","volume":"55","author":"GA Buzzell","year":"2022","unstructured":"Buzzell, G.A., Niu, Y., Aviyente, S., Bernat, E.: A practical introduction to EEG time-frequency principal components analysis (TF-PCA). Dev. Cogn. Neurosci. 55, 101114 (2022)","journal-title":"Dev. Cogn. Neurosci."},{"issue":"12","key":"39_CR13","doi-asserted-by":"publisher","first-page":"8659","DOI":"10.1016\/j.eswa.2010.06.065","volume":"37","author":"A Subasi","year":"2010","unstructured":"Subasi, A., Gursoy, M.I.: EEG signal classification using PCA, ICA, LDA and support vector machines. Expert Syst. Appl. 37(12), 8659\u20138666 (2010)","journal-title":"Expert Syst. Appl."},{"key":"39_CR14","doi-asserted-by":"publisher","first-page":"770","DOI":"10.3389\/fnins.2018.00770","volume":"12","author":"M Grieder","year":"2018","unstructured":"Grieder, M., Wang, D.J., Dierks, T., Wahlund, L.-O., Jann, K.: Default mode network complexity and cognitive decline in mild Alzheimer\u2019s disease. Front. Neurosci. 12, 770 (2018)","journal-title":"Front. Neurosci."},{"key":"39_CR15","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1007\/s11571-014-9325-x","volume":"9","author":"R Wang","year":"2015","unstructured":"Wang, R., Wang, J., Yu, H., Wei, X., Yang, C., Deng, B.: Power spectral density and coherence analysis of Alzheimer\u2019s EEG. Cogn. Neurodyn. 9, 291\u2013304 (2015)","journal-title":"Cogn. Neurodyn."},{"issue":"4","key":"39_CR16","doi-asserted-by":"publisher","first-page":"1477","DOI":"10.3390\/s22041477","volume":"22","author":"Z Sverko","year":"2022","unstructured":"Sverko, Z., Vranki\u0107, M., Vlahini\u0107, S., Rogelj, P.: Complex Pearson correlation coefficient for EEG connectivity analysis. Sensors 22(4), 1477 (2022)","journal-title":"Sensors"},{"issue":"3","key":"39_CR17","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1080\/02699931.2023.2297272","volume":"38","author":"J Liu","year":"2024","unstructured":"Liu, J., Hu, X., Shen, X., Song, S., Zhang, D.: Electrophysiological representations of multivariate human emotion experience. Cogn. Emot. 38(3), 378\u2013388 (2024)","journal-title":"Cogn. Emot."},{"issue":"11","key":"39_CR18","doi-asserted-by":"publisher","first-page":"2169","DOI":"10.1016\/j.clinph.2011.03.023","volume":"122","author":"JH Roh","year":"2011","unstructured":"Roh, J.H., et al.: Region and frequency specific changes of spectral power in Alzheimer\u2019s disease and mild cognitive impairment. Clin. Neurophysiol. 122(11), 2169\u20132176 (2011)","journal-title":"Clin. Neurophysiol."},{"key":"39_CR19","doi-asserted-by":"crossref","unstructured":"Liu, H., Cai, M., Lee, Y.J.: Masked discrimination for self-supervised learning on point clouds. In:\u00a0European Conference on Computer Vision, pp. 657\u2013675. Springer, Cham (2022)","DOI":"10.1007\/978-3-031-20086-1_38"},{"key":"39_CR20","doi-asserted-by":"publisher","first-page":"101298","DOI":"10.1016\/j.csl.2021.101298","volume":"72","author":"F Bertini","year":"2022","unstructured":"Bertini, F., Allevi, D., Lutero, G., Calza, L., Montesi, D.: An automatic Alzheimer\u2019s disease classifier based on spontaneous spoken English. Comput. Speech Lang. 72, 101298 (2022)","journal-title":"Comput. Speech Lang."},{"key":"39_CR21","doi-asserted-by":"crossref","unstructured":"Taud, H., Mas, J.-F.: Multilayer perceptron (MLP). In:\u00a0Geomatic Approaches for Modeling Land Change Scenarios, pp. 451\u2013455. Springer, Cham (2018)","DOI":"10.1007\/978-3-319-60801-3_27"},{"key":"39_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":"39_CR23","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1016\/j.inffus.2020.01.008","volume":"59","author":"H Cai","year":"2020","unstructured":"Cai, H., Qu, Z., Li, Z., Zhang, Y., Hu, X., Hu, B.: Feature-level fusion approaches based on multimodal EEG data for depression recognition. Inf. Fusion 59, 127\u2013138 (2020)","journal-title":"Inf. Fusion"},{"issue":"1","key":"39_CR24","doi-asserted-by":"publisher","first-page":"35","DOI":"10.3390\/e20010035","volume":"20","author":"SJ Ruiz-Gomez","year":"2018","unstructured":"Ruiz-Gomez, S.J., et al.: Automated multiclass classification of spontaneous EEG activity in Alzheimer\u2019s disease and mild cognitive impairment. Entropy 20(1), 35 (2018)","journal-title":"Entropy"},{"issue":"3","key":"39_CR25","doi-asserted-by":"publisher","first-page":"647","DOI":"10.1007\/s11571-022-09859-2","volume":"17","author":"S Dogan","year":"2023","unstructured":"Dogan, S., et al.: Primate brain pattern-based automated Alzheimer\u2019s disease detection model using EEG signals. Cogn. Neurodyn. 17(3), 647\u2013659 (2023)","journal-title":"Cogn. Neurodyn."},{"key":"39_CR26","doi-asserted-by":"publisher","first-page":"84553","DOI":"10.1109\/ACCESS.2023.3294618","volume":"11","author":"A Miltiadous","year":"2023","unstructured":"Miltiadous, A., Gionanidis, E., Tzimourta, K.D., Giannakeas, N., Tzallas, A.T.: Dice-Net: a novel convolution-transformer architecture for Alzheimer detection in EEG signals. IEEE Access 11, 84553\u201384564 (2023)","journal-title":"IEEE Access"}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-0033-8_39","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T22:14:27Z","timestamp":1781129667000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-0033-8_39"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819500321","9789819500338"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-0033-8_39","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"25 July 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"All authors declare that they have no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ningbo","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/icg\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}