{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T18:42:15Z","timestamp":1772044935018,"version":"3.50.1"},"publisher-location":"Cham","reference-count":39,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031781940","type":"print"},{"value":"9783031781957","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T00:00:00Z","timestamp":1733184000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T00:00:00Z","timestamp":1733184000000},"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\/978-3-031-78195-7_18","type":"book-chapter","created":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T11:11:10Z","timestamp":1733137870000},"page":"270-283","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Attention Dynamics: Estimating Attention Levels of ADHD using Swin Transformer"],"prefix":"10.1007","author":[{"given":"Debashis Das","family":"Chakladar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anand","family":"Shankar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Foteini","family":"Liwicki","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shovan","family":"Barma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rajkumar","family":"Saini","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,3]]},"reference":[{"issue":"1","key":"18_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2017.181","volume":"4","author":"LM Alexander","year":"2017","unstructured":"Alexander, L.M., Escalera, J., Ai, L., Andreotti, C., Febre, K., Mangone, A., Vega-Potler, N., Langer, N., Alexander, A., Kovacs, M., et al.: An open resource for transdiagnostic research in pediatric mental health and learning disorders. Scientific data 4(1), 1\u201326 (2017)","journal-title":"Scientific data"},{"key":"18_CR2","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2021.705103","volume":"15","author":"A Boschi","year":"2021","unstructured":"Boschi, A., Brofiga, M., Massobrio, P.: Thresholding functional connectivity matrices to recover the topological properties of large-scale neuronal networks. Front. Neurosci. 15, 705103 (2021)","journal-title":"Front. Neurosci."},{"issue":"1","key":"18_CR3","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1038\/s41398-023-02536-w","volume":"13","author":"M Cao","year":"2023","unstructured":"Cao, M., Martin, E., Li, X.: Machine learning in attention-deficit\/hyperactivity disorder: new approaches toward understanding the neural mechanisms. Transl. Psychiatry 13(1), 236 (2023)","journal-title":"Transl. Psychiatry"},{"key":"18_CR4","doi-asserted-by":"crossref","unstructured":"Chakladar, D.D., Datta, S., Roy, P.P., Vinod, A.: Cognitive workload estimation using variational auto encoder & attention-based deep model. IEEE Transactions on Cognitive and Developmental Systems (2022)","DOI":"10.1109\/TCDS.2022.3163020"},{"key":"18_CR5","doi-asserted-by":"crossref","unstructured":"Chakladar, D.D., Pal, N.R.: Brain connectivity analysis for EEG-based face perception task. IEEE Transactions on Cognitive and Developmental Systems (2024)","DOI":"10.1109\/TCDS.2024.3370635"},{"issue":"4","key":"18_CR6","doi-asserted-by":"publisher","first-page":"1507","DOI":"10.1109\/TCDS.2021.3116079","volume":"14","author":"DD Chakladar","year":"2021","unstructured":"Chakladar, D.D., Roy, P.P., Iwamura, M.: EEG-based cognitive state classification and analysis of brain dynamics using deep ensemble model and graphical brain network. IEEE Transactions on Cognitive and Developmental Systems 14(4), 1507\u20131519 (2021)","journal-title":"IEEE Transactions on Cognitive and Developmental Systems"},{"key":"18_CR7","doi-asserted-by":"crossref","unstructured":"Chakladar, D.D., Samanta, D., Roy, P.P.: Multimodal deep sparse subspace clustering for multiple stimuli-based cognitive task. In: 2022 26th International Conference on Pattern Recognition (ICPR). pp. 1098\u20131104. IEEE (2022)","DOI":"10.1109\/ICPR56361.2022.9955632"},{"key":"18_CR8","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.neucom.2019.04.058","volume":"356","author":"H Chen","year":"2019","unstructured":"Chen, H., Song, Y., Li, X.: A deep learning framework for identifying children with adhd using an EEG-based brain network. Neurocomputing 356, 83\u201396 (2019)","journal-title":"Neurocomputing"},{"issue":"8","key":"18_CR9","doi-asserted-by":"publisher","first-page":"1256","DOI":"10.1016\/j.clinph.2019.05.001","volume":"130","author":"AR Clarke","year":"2019","unstructured":"Clarke, A.R., Barry, R.J., Johnstone, S.J., McCarthy, R., Selikowitz, M.: EEG development in attention deficit hyperactivity disorder: From child to adult. Clin. Neurophysiol. 130(8), 1256\u20131262 (2019)","journal-title":"Clin. Neurophysiol."},{"key":"18_CR10","doi-asserted-by":"crossref","unstructured":"Cohen, M.X.: Analyzing neural time series data: theory and practice. MIT press (2014)","DOI":"10.7551\/mitpress\/9609.001.0001"},{"key":"18_CR11","doi-asserted-by":"crossref","unstructured":"Criaud, M., Wulff, M., Alegria, A., Barker, G., Giampietro, V., Rubia, K.: Increased left inferior fronto-striatal activation during error monitoring after fMRI neurofeedback of right inferior frontal cortex in adolescents with attention deficit hyperactivity disorder. NeuroImage: Clinical 27, 102311 (2020)","DOI":"10.1016\/j.nicl.2020.102311"},{"key":"18_CR12","doi-asserted-by":"crossref","unstructured":"Dong, Q., Qiang, N., Lv, J., Li, X., Liu, T., Li, Q.: Spatiotemporal attention autoencoder (STAAE) for ADHD classification. In: Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2020: 23rd International Conference, Lima, Peru, October 4\u20138, 2020, Proceedings, Part VII 23. pp. 508\u2013517. Springer (2020)","DOI":"10.1007\/978-3-030-59728-3_50"},{"key":"18_CR13","doi-asserted-by":"publisher","first-page":"251","DOI":"10.3389\/fnins.2020.00251","volume":"14","author":"L Dubreuil-Vall","year":"2020","unstructured":"Dubreuil-Vall, L., Ruffini, G., Camprodon, J.A.: Deep learning convolutional neural networks discriminate adult ADHD from healthy individuals on the basis of event-related spectral EEG. Front. Neurosci. 14, 251 (2020)","journal-title":"Front. Neurosci."},{"issue":"3","key":"18_CR14","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/acc902","volume":"20","author":"MY Esas","year":"2023","unstructured":"Esas, M.Y., Latifo\u011flu, F.: Detection of adhd from EEG signals using new hybrid decomposition and deep learning techniques. J. Neural Eng. 20(3), 036028 (2023)","journal-title":"J. Neural Eng."},{"issue":"5","key":"18_CR15","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/acf7f5","volume":"20","author":"Y He","year":"2023","unstructured":"He, Y., Wang, X., Yang, Z., Xue, L., Chen, Y., Ji, J., Wan, F., Mukhopadhyay, S.C., Men, L., Tong, M.C.F., et al.: Classification of attention deficit\/hyperactivity disorder based on eeg signals using a EEG-transformer model. J. Neural Eng. 20(5), 056013 (2023)","journal-title":"J. Neural Eng."},{"key":"18_CR16","doi-asserted-by":"crossref","unstructured":"Hong, J., Park, B.y., Cho, H.h., Park, H.: Age-related connectivity differences between attention deficit and hyperactivity disorder patients and typically developing subjects: a resting-state functional MRI study. Neural regeneration research 12(10), 1640 (2017)","DOI":"10.4103\/1673-5374.217339"},{"key":"18_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.103452","volume":"74","author":"AM Judith","year":"2022","unstructured":"Judith, A.M., Priya, S.B., Mahendran, R.K.: Artifact removal from EEG signals using regenerative multi-dimensional singular value decomposition and independent component analysis. Biomed. Signal Process. Control 74, 103452 (2022)","journal-title":"Biomed. Signal Process. Control"},{"issue":"10","key":"18_CR18","doi-asserted-by":"publisher","first-page":"2095","DOI":"10.1111\/ejn.14645","volume":"51","author":"H Kiiski","year":"2020","unstructured":"Kiiski, H., Bennett, M., Rueda-Delgado, L.M., Farina, F.R., Knight, R., Boyle, R., Roddy, D., Grogan, K., Bramham, J., Kelly, C., et al.: EEG spectral power, but not theta\/beta ratio, is a neuromarker for adult ADHD. Eur. J. Neurosci. 51(10), 2095\u20132109 (2020)","journal-title":"Eur. J. Neurosci."},{"issue":"6","key":"18_CR19","doi-asserted-by":"publisher","first-page":"904","DOI":"10.1002\/hbm.21058","volume":"31","author":"K Konrad","year":"2010","unstructured":"Konrad, K., Eickhoff, S.B.: Is the ADHD brain wired differently? a review on structural and functional connectivity in attention deficit hyperactivity disorder. Hum. Brain Mapp. 31(6), 904\u2013916 (2010)","journal-title":"Hum. Brain Mapp."},{"issue":"1","key":"18_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2017.40","volume":"4","author":"N Langer","year":"2017","unstructured":"Langer, N., Ho, E.J., Alexander, L.M., Xu, H.Y., Jozanovic, R.K., Henin, S., Petroni, A., Cohen, S., Marcelle, E.T., Parra, L.C., et al.: A resource for assessing information processing in the developing brain using EEG and eye tracking. Scientific data 4(1), 1\u201320 (2017)","journal-title":"Scientific data"},{"key":"18_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.brainresbull.2023.110834","volume":"206","author":"Z Li","year":"2024","unstructured":"Li, Z., Zhang, R., Zeng, Y., Tong, L., Lu, R., Yan, B.: Mst-net: A multi-scale swin transformer network for EEG-based cognitive load assessment. Brain Res. Bull. 206, 110834 (2024)","journal-title":"Brain Res. Bull."},{"key":"18_CR22","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF international conference on computer vision. pp. 10012\u201310022 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"18_CR23","doi-asserted-by":"publisher","first-page":"853","DOI":"10.1016\/j.neuroimage.2013.08.056","volume":"85","author":"M Lobier","year":"2014","unstructured":"Lobier, M., Siebenh\u00fchner, F., Palva, S., Palva, J.M.: Phase transfer entropy: a novel phase-based measure for directed connectivity in networks coupled by oscillatory interactions. Neuroimage 85, 853\u2013872 (2014)","journal-title":"Neuroimage"},{"issue":"1","key":"18_CR24","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1177\/1550059416643824","volume":"48","author":"S Markovska-Simoska","year":"2017","unstructured":"Markovska-Simoska, S., Pop-Jordanova, N.: Quantitative in children and adults with attention deficit hyperactivity disorder: comparison of absolute and relative power spectra and theta\/beta ratio. Clin. EEG Neurosci. 48(1), 20\u201332 (2017)","journal-title":"Clin. EEG Neurosci."},{"key":"18_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105738","volume":"197","author":"M Moghaddari","year":"2020","unstructured":"Moghaddari, M., Lighvan, M.Z., Danishvar, S.: Diagnose ADHD disorder in children using convolutional neural network based on continuous mental task EEG. Comput. Methods Programs Biomed. 197, 105738 (2020)","journal-title":"Comput. Methods Programs Biomed."},{"key":"18_CR26","doi-asserted-by":"crossref","unstructured":"Panda, D., Chakladar, D.D., Dasgupta, T.: Multimodal system for emotion recognition using EEG and customer review. In: Proceedings of the global ai congress 2019. pp. 399\u2013410. Springer (2020)","DOI":"10.1007\/978-981-15-2188-1_32"},{"issue":"1","key":"18_CR27","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1002\/cb.2142","volume":"23","author":"D Panda","year":"2024","unstructured":"Panda, D., Chakladar, D.D., Rana, S., Parayitam, S.: An EEG-based neuro-recommendation system for improving consumer purchase experience. J. Consum. Behav. 23(1), 61\u201375 (2024)","journal-title":"J. Consum. Behav."},{"key":"18_CR28","doi-asserted-by":"crossref","unstructured":"Panda, D., Chakladar, D.D., Rana, S., Shamsudin, M.N.: Spatial attention-enhanced EEG analysis for profiling consumer choices. IEEE Access (2024)","DOI":"10.1109\/ACCESS.2024.3355977"},{"key":"18_CR29","doi-asserted-by":"crossref","unstructured":"Qi, N., Piao, Y., Zhang, H., Wang, Q., Wang, Y.: Seizure prediction based on improved vision transformer model for EEG channel optimization. Computer Methods in Biomechanics and Biomedical Engineering pp. 1\u201312 (2024)","DOI":"10.1080\/10255842.2024.2326097"},{"key":"18_CR30","doi-asserted-by":"publisher","first-page":"704","DOI":"10.1007\/s10548-018-0691-2","volume":"32","author":"M Rubega","year":"2019","unstructured":"Rubega, M., Carboni, M., Seeber, M., Pascucci, D., Tourbier, S., Toscano, G., Van Mierlo, P., Hagmann, P., Plomp, G., Vulliemoz, S., et al.: Estimating EEG source dipole orientation based on singular-value decomposition for connectivity analysis. Brain Topogr. 32, 704\u2013719 (2019)","journal-title":"Brain Topogr."},{"key":"18_CR31","doi-asserted-by":"publisher","first-page":"100","DOI":"10.3389\/fnhum.2018.00100","volume":"12","author":"K Rubia","year":"2018","unstructured":"Rubia, K.: Cognitive neuroscience of attention deficit hyperactivity disorder (ADHD) and its clinical translation. Front. Hum. Neurosci. 12, 100 (2018)","journal-title":"Front. Hum. Neurosci."},{"key":"18_CR32","doi-asserted-by":"publisher","first-page":"5944","DOI":"10.1109\/TIP.2021.3090531","volume":"30","author":"M Scetbon","year":"2021","unstructured":"Scetbon, M., Elad, M., Milanfar, P.: Deep k-svd denoising. IEEE Trans. Image Process. 30, 5944\u20135955 (2021)","journal-title":"IEEE Trans. Image Process."},{"key":"18_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.neubiorev.2022.104752","volume":"139","author":"J Slater","year":"2022","unstructured":"Slater, J., Joober, R., Koborsy, B.L., Mitchell, S., Sahlas, E., Palmer, C.: Can electroencephalography (EEG) identify ADHD subtypes? a systematic review. Neuroscience & Biobehavioral Reviews 139, 104752 (2022)","journal-title":"Neuroscience & Biobehavioral Reviews"},{"key":"18_CR34","doi-asserted-by":"crossref","unstructured":"Tadel, F., Baillet, S., Mosher, J.C., Pantazis, D., Leahy, R.M.: Brainstorm: a user-friendly application for MEG\/EEG analysis. Computational intelligence and neuroscience 2011 (2011)","DOI":"10.1155\/2011\/879716"},{"issue":"3","key":"18_CR35","doi-asserted-by":"publisher","first-page":"693","DOI":"10.1007\/s13246-021-01018-x","volume":"44","author":"M Tosun","year":"2021","unstructured":"Tosun, M.: Effects of spectral features of EEG signals recorded with different channels and recording statuses on adhd classification with deep learning. Physical and Engineering Sciences in Medicine 44(3), 693\u2013702 (2021)","journal-title":"Physical and Engineering Sciences in Medicine"},{"issue":"7","key":"18_CR36","doi-asserted-by":"publisher","first-page":"1055","DOI":"10.3390\/jcm8071055","volume":"8","author":"A Vahid","year":"2019","unstructured":"Vahid, A., Bluschke, A., Roessner, V., Stober, S., Beste, C.: Deep learning based on event-related EEG differentiates children with ADHD from healthy controls. J. Clin. Med. 8(7), 1055 (2019)","journal-title":"J. Clin. Med."},{"key":"18_CR37","doi-asserted-by":"crossref","unstructured":"Wang, H., Cao, L., Huang, C., Jia, J., Dong, Y., Fan, C., De\u00a0Albuquerque, V.H.C.: A novel algorithmic structure of EEG channel attention combined with swin transformer for motor patterns classification. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023)","DOI":"10.1109\/TNSRE.2023.3297654"},{"issue":"7","key":"18_CR38","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1016\/j.braindev.2019.03.006","volume":"41","author":"A Yasumura","year":"2019","unstructured":"Yasumura, A., Omori, M., Fukuda, A., Takahashi, J., Yasumura, Y., Nakagawa, E., Koike, T., Yamashita, Y., Miyajima, T., Koeda, T., et al.: Age-related differences in frontal lobe function in children with ADHD. Brain Develop. 41(7), 577\u2013586 (2019)","journal-title":"Brain Develop."},{"key":"18_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.103349","volume":"72","author":"M Yu","year":"2022","unstructured":"Yu, M., Xiao, S., Hua, M., Wang, H., Chen, X., Tian, F., Li, Y.: EEG-based emotion recognition in an immersive virtual reality environment: From local activity to brain network features. Biomed. Signal Process. Control 72, 103349 (2022)","journal-title":"Biomed. Signal Process. Control"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-78195-7_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T12:06:44Z","timestamp":1733141204000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-78195-7_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,3]]},"ISBN":["9783031781940","9783031781957"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-78195-7_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,3]]},"assertion":[{"value":"3 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kolkata","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpr2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icpr2024.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}