{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T16:18:27Z","timestamp":1772554707189,"version":"3.50.1"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,3,23]],"date-time":"2024-03-23T00:00:00Z","timestamp":1711152000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,3,23]],"date-time":"2024-03-23T00:00:00Z","timestamp":1711152000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Ministry of Higher Education and Scientific Research of Tunisia","award":["LR11ES48"],"award-info":[{"award-number":["LR11ES48"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cogn Comput"],"published-print":{"date-parts":[[2024,5]]},"DOI":"10.1007\/s12559-024-10261-9","type":"journal-article","created":{"date-parts":[[2024,3,23]],"date-time":"2024-03-23T06:35:07Z","timestamp":1711175707000},"page":"1268-1286","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A Mutual Information-Based Many-Objective Optimization Method for EEG Channel Selection in the Epileptic Seizure Prediction Task"],"prefix":"10.1007","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6764-1588","authenticated-orcid":false,"given":"Najwa","family":"Kouka","sequence":"first","affiliation":[]},{"given":"Rahma","family":"Fourati","sequence":"additional","affiliation":[]},{"given":"Asma","family":"Baghdadi","sequence":"additional","affiliation":[]},{"given":"Patrick","family":"Siarry","sequence":"additional","affiliation":[]},{"given":"M.","family":"Adel","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,3,23]]},"reference":[{"key":"10261_CR1","doi-asserted-by":"publisher","unstructured":"Hazarika BB, Gupta D, Kumar B. EEG signal classification using a novel Universum-based twin parametric-margin support vector machine. Cogn Comput. 2023. https:\/\/doi.org\/10.1007\/s12559-023-10115-w.","DOI":"10.1007\/s12559-023-10115-w"},{"issue":"8","key":"10261_CR2","doi-asserted-by":"publisher","first-page":"8519","DOI":"10.1007\/s12652-020-02586-8","volume":"12","author":"A Baghdadi","year":"2021","unstructured":"Baghdadi A, Aribi Y, Fourati R, et al. Psychological stimulation for anxious states detection based on EEG-related features. J Ambient Intell Humaniz Comput. 2021;12(8):8519\u201333.","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"10261_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/IJCNN48605.2020.9207070","volume-title":"2020 International Joint Conference on Neural Networks (IJCNN)","author":"A Baghdadi","year":"2020","unstructured":"Baghdadi A, Fourati R, Aribi Y, et al. Robust feature learning method for epileptic seizures prediction based on long-term EEG signals. In: 2020 International Joint Conference on Neural Networks (IJCNN). 2020. p. 1\u20137. https:\/\/doi.org\/10.1109\/IJCNN48605.2020.9207070."},{"issue":"7","key":"10261_CR4","doi-asserted-by":"publisher","first-page":"9403","DOI":"10.1007\/s12652-023-04609-6","volume":"14","author":"A Baghdadi","year":"2023","unstructured":"Baghdadi A, Fourati R, Aribi Y, et al. A channel-wise attention-based representation learning method for epileptic seizure detection and type classification. J Ambient Intell Humaniz Comput. 2023;14(7):9403\u201318.","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"10261_CR5","unstructured":"World Health Organization. Epilepsy. 2022. https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/epilepsy. Accessed 3 Feb 2023."},{"key":"10261_CR6","doi-asserted-by":"publisher","first-page":"10637","DOI":"10.1007\/s00521-023-08254-0","volume":"35","author":"K Visalini","year":"2023","unstructured":"Visalini K, Alagarsamy S, Nagarajan D. Neonatal seizure detection using deep belief networks from multichannel EEG data. Neural Comput Appl. 2023;35:10637\u201347.","journal-title":"Neural Comput. Appl."},{"key":"10261_CR7","doi-asserted-by":"publisher","unstructured":"Wu D, Li J, Dong F, et al. Classification of seizure types based on multi-class specific bands common spatial pattern and penalized ensemble model. Biomed Signal Process Control. 2023;79:104118. https:\/\/doi.org\/10.1016\/j.bspc.2022.104118. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1746809422005742.","DOI":"10.1016\/j.bspc.2022.104118"},{"key":"10261_CR8","doi-asserted-by":"publisher","unstructured":"Affes A, Mdhaffar A, Triki C, et al. Personalized attention-based EEG channel selection for epileptic seizure prediction. Expert Syst Appl. 2022;206:117733. https:\/\/doi.org\/10.1016\/j.eswa.2022.117733. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0957417422010144.","DOI":"10.1016\/j.eswa.2022.117733"},{"key":"10261_CR9","doi-asserted-by":"publisher","first-page":"54112","DOI":"10.1109\/ACCESS.2023.3281450","volume":"11","author":"R Jana","year":"2023","unstructured":"Jana R, Mukherjee I. Efficient seizure prediction and EEG channel selection based on multi-objective optimization. IEEE Access. 2023;11:54112\u201321. https:\/\/doi.org\/10.1109\/ACCESS.2023.3281450.","journal-title":"IEEE Access."},{"issue":"3","key":"10261_CR10","doi-asserted-by":"publisher","first-page":"569","DOI":"10.1016\/j.clinph.2004.08.025","volume":"116","author":"F Mormann","year":"2005","unstructured":"Mormann F, Kreuz T, Rieke C, et al. On the predictability of epileptic seizures. Clin Neurophysiol. 2005;116(3):569\u201387.","journal-title":"Clin. Neurophysiol."},{"key":"10261_CR11","doi-asserted-by":"publisher","unstructured":"Jana R, Mukherjee I. Deep learning based efficient epileptic seizure prediction with EEG channel optimization. Biomed Signal Process Control. 2021;68(102):767. https:\/\/doi.org\/10.1016\/j.bspc.2021.102767. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1746809421003645.","DOI":"10.1016\/j.bspc.2021.102767"},{"issue":"1","key":"10261_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-022-08322-w","volume":"12","author":"M Pinto","year":"2022","unstructured":"Pinto M, Coelho T, Leal A, et al. Interpretable EEG seizure prediction using a multiobjective evolutionary algorithm. Sci Rep. 2022;12(1):1\u201315.","journal-title":"Sci. Rep."},{"issue":"2","key":"10261_CR13","doi-asserted-by":"publisher","first-page":"900","DOI":"10.1109\/JBHI.2022.3221211","volume":"27","author":"Y Wang","year":"2023","unstructured":"Wang Y, Shi Y, Cheng Y, et al. A spatiotemporal graph attention network based on synchronization for epileptic seizure prediction. IEEE J Biomed Health Inform. 2023;27(2):900\u201311. https:\/\/doi.org\/10.1109\/JBHI.2022.3221211.","journal-title":"IEEE J Biomed Health Inform."},{"key":"10261_CR14","doi-asserted-by":"publisher","unstructured":"Ra JS, Li T, YanLi. A novel epileptic seizure prediction method based on synchroextracting transform and 1-dimensional convolutional neural network. Comput Methods Programs Biomed. 2023;240:107678. https:\/\/doi.org\/10.1016\/j.cmpb.2023.107678. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0169260723003437.","DOI":"10.1016\/j.cmpb.2023.107678"},{"issue":"1","key":"10261_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-021-82828-7","volume":"11","author":"M Pinto","year":"2021","unstructured":"Pinto M, Leal A, Lopes F, et al. A personalized and evolutionary algorithm for interpretable EEG epilepsy seizure prediction. Sci Rep. 2021;11(1):1\u201312.","journal-title":"Sci. Rep."},{"issue":"23","key":"10261_CR16","doi-asserted-by":"publisher","first-page":"7972","DOI":"10.3390\/s21237972","volume":"21","author":"JS Ra","year":"2021","unstructured":"Ra JS, Li T, Li Y. A novel permutation entropy-based EEG channel selection for improving epileptic seizure prediction. Sensors. 2021;21(23):7972.","journal-title":"Sensors"},{"key":"10261_CR17","doi-asserted-by":"publisher","first-page":"164348","DOI":"10.1109\/ACCESS.2021.3134166","volume":"9","author":"A Romney","year":"2021","unstructured":"Romney A, Manian V. Optimizing seizure prediction from reduced scalp EEG channels based on spectral features and MAML. IEEE Access. 2021;9:164348\u201357. https:\/\/doi.org\/10.1109\/ACCESS.2021.3134166.","journal-title":"IEEE Access"},{"key":"10261_CR18","doi-asserted-by":"publisher","unstructured":"Li R, Ren C, Zhang X, et al. A novel ensemble learning method using multiple objective particle swarm optimization for subject-independent EEG-based emotion recognition. Comput Biol Med. 2022;140:105080. https:\/\/doi.org\/10.1016\/j.compbiomed.2021.105080. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S001048252100874X.","DOI":"10.1016\/j.compbiomed.2021.105080"},{"issue":"1","key":"10261_CR19","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1080\/2326263X.2021.1966985","volume":"9","author":"P Sheoran","year":"2022","unstructured":"Sheoran P, Saini J. Optimizing channel selection using multi-objective FODPSO for BCI applications. Brain-Computer Interfaces. 2022;9(1):7\u201322. https:\/\/doi.org\/10.1080\/2326263X.2021.1966985.","journal-title":"Brain-Computer Interfaces."},{"key":"10261_CR20","doi-asserted-by":"publisher","first-page":"1942","DOI":"10.1109\/ICNN.1995.488968","volume-title":"Proceedings of ICNN\u201995 - International Conference on Neural Networks","author":"J Kennedy","year":"1995","unstructured":"Kennedy J, Eberhart R. Particle swarm optimization. In: Proceedings of ICNN\u201995 - International Conference on Neural Networks, vol. 4. 1995. p. 1942\u20138. https:\/\/doi.org\/10.1109\/ICNN.1995.488968."},{"key":"10261_CR21","doi-asserted-by":"publisher","first-page":"4104","DOI":"10.1109\/ICSMC.1997.637339","volume-title":"1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation","author":"J Kennedy","year":"1997","unstructured":"Kennedy J, Eberhart R. A discrete binary version of the particle swarm algorithm. In: 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation, vol. 5. 1997. p. 4104\u20138. https:\/\/doi.org\/10.1109\/ICSMC.1997.637339."},{"key":"10261_CR22","doi-asserted-by":"publisher","unstructured":"Hu W, Cao e, Lai X, et\u00a0al. Mean amplitude spectrum based epileptic state classification for seizure prediction using convolutional neural networks. J Ambient Intell Humaniz Comput. 2023;14:15485\u201395. https:\/\/doi.org\/10.1007\/s12652-019-01220-6.","DOI":"10.1007\/s12652-019-01220-6"},{"issue":"9","key":"10261_CR23","doi-asserted-by":"publisher","first-page":"2109","DOI":"10.1109\/TBME.2017.2785401","volume":"65","author":"H Khan","year":"2018","unstructured":"Khan H, Marcuse L, Fields M, et al. Focal onset seizure prediction using convolutional networks. IEEE Trans Biomed Eng. 2018;65(9):2109\u201318. https:\/\/doi.org\/10.1109\/TBME.2017.2785401.","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"11","key":"10261_CR24","doi-asserted-by":"publisher","first-page":"5516","DOI":"10.3390\/app12115516","volume":"12","author":"RI Halawa","year":"2022","unstructured":"Halawa RI, Youssef SM, Elagamy MN. An efficient hybrid model for patient-independent seizure prediction using deep learning. Appl Sci. 2022;12(11):5516.","journal-title":"Appl. Sci."},{"issue":"2","key":"10261_CR25","first-page":"26","volume":"16","author":"J Nazari","year":"2023","unstructured":"Nazari J, Nasrabadi AM, Menhaj MB, et al. Epileptic seizure prediction using multi-channel raw EEGs with convolutional neural network. J Robot Syst. 2023;16(2):26\u201335.","journal-title":"J Robot Syst"},{"issue":"19","key":"10261_CR26","doi-asserted-by":"publisher","first-page":"23133","DOI":"10.1109\/JSEN.2023.3307223","volume":"23","author":"T Mao","year":"2023","unstructured":"Mao T, Li C, Zhao Y, Song R, Chen X. Online test-time adaptation for patient-independent seizure prediction. IEEE Sens J. 2023;23(19):23133\u201344. https:\/\/doi.org\/10.1109\/JSEN.2023.3307223.","journal-title":"IEEE Sens J."},{"issue":"7","key":"10261_CR27","doi-asserted-by":"publisher","first-page":"9377","DOI":"10.1109\/JSEN.2021.3057076","volume":"21","author":"T Dissanayake","year":"2021","unstructured":"Dissanayake T, Fernando T, Denman S, et al. Deep learning for patient-independent epileptic seizure prediction using scalp EEG signals. IEEE Sens J. 2021;21(7):9377\u201388.","journal-title":"IEEE Sens. J."},{"issue":"2","key":"10261_CR28","doi-asserted-by":"publisher","first-page":"527","DOI":"10.1109\/JBHI.2021.3100297","volume":"26","author":"T Dissanayake","year":"2022","unstructured":"Dissanayake T, Fernando T, Denman S, et al. Geometric deep learning for subject independent epileptic seizure prediction using scalp EEG signals. IEEE J Biomed Health Inform. 2022;26(2):527\u201338.","journal-title":"IEEE J Biomed Health Inform."},{"key":"10261_CR29","unstructured":"Shi X, Chen Z, Wang H, et al. Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In: Cortes C, Lawrence ND, Lee DD, et al., editors. Advances in Neural Infor-mation Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, December 7\u201312, 2015, Montreal, Quebec, Canada. 2015. p. 802\u201310. http:\/\/papers.nips.cc\/paper\/5955-convolutional-lstm-network-a-machine-learning-approach-for-precipitation-nowcasting."},{"key":"10261_CR30","unstructured":"Shoeb A. CHB-MIT Scalp EEG Database. 2010. https:\/\/physionet.org\/content\/chbmit\/1.0.0\/. Accessed 3 Jan 2022."},{"key":"10261_CR31","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1016\/j.yebeh.2015.03.010","volume":"46","author":"M Bandarabadi","year":"2015","unstructured":"Bandarabadi M, Rasekhi J, Teixeira CA, et al. On the proper selection of preictal period for seizure prediction. Epilepsy Behav. 2015;46:158\u201366.","journal-title":"Epilepsy Behav"},{"key":"10261_CR32","doi-asserted-by":"publisher","first-page":"1191683","DOI":"10.3389\/fnins.2023.1191683","volume":"17","author":"X Jiang","year":"2023","unstructured":"Jiang X, Liu X, Liu Y, et al. Epileptic seizures detection and the analysis of optimal seizure prediction horizon based on frequency and phase analysis. Front Neurosci. 2023;17:1191683.","journal-title":"Front. Neurosci."},{"issue":"5","key":"10261_CR33","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1177\/1550059420966436","volume":"52","author":"E Bergil","year":"2021","unstructured":"Bergil E, OCBozkurt MR. An evaluation of the channel effect on detecting the preictal stage in patients with epilepsy. Clin EEG Neurosci. 2021;52(5):376\u201385.","journal-title":"Clin. EEG Neurosci."}],"container-title":["Cognitive Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12559-024-10261-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12559-024-10261-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12559-024-10261-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T09:35:49Z","timestamp":1717234549000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12559-024-10261-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,23]]},"references-count":33,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,5]]}},"alternative-id":["10261"],"URL":"https:\/\/doi.org\/10.1007\/s12559-024-10261-9","relation":{},"ISSN":["1866-9956","1866-9964"],"issn-type":[{"value":"1866-9956","type":"print"},{"value":"1866-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,23]]},"assertion":[{"value":"20 February 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 February 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 March 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed Consent"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}