{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T12:31:14Z","timestamp":1764333074830,"version":"3.37.3"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"20","license":[{"start":{"date-parts":[[2021,9,25]],"date-time":"2021-09-25T00:00:00Z","timestamp":1632528000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,9,25]],"date-time":"2021-09-25T00:00:00Z","timestamp":1632528000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100012554","name":"Foundation for Distinguished Young Talents in Higher Education of Henan","doi-asserted-by":"publisher","award":["2021-ZZJH-200"],"award-info":[{"award-number":["2021-ZZJH-200"]}],"id":[{"id":"10.13039\/501100012554","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2023,7]]},"DOI":"10.1007\/s00521-021-06118-z","type":"journal-article","created":{"date-parts":[[2021,9,25]],"date-time":"2021-09-25T08:02:55Z","timestamp":1632556975000},"page":"14577-14589","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["An improved deep learning mechanism for EEG recognition in sports health informatics"],"prefix":"10.1007","volume":"35","author":[{"given":"Tong","family":"Zhao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingyi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zuocan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ryan","family":"Alturki","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,9,25]]},"reference":[{"issue":"5","key":"6118_CR1","doi-asserted-by":"publisher","first-page":"1319","DOI":"10.1007\/s00521-012-1074-3","volume":"23","author":"A Ahangi","year":"2013","unstructured":"Ahangi A, Karamnejad M, Mohammadi N, Ebrahimpour R, Bagheri N (2013) Multiple classifier system for EEG signal classification with application to brain\u2013computer interfaces. Neural Comput Appl 23(5):1319\u20131327","journal-title":"Neural Comput Appl"},{"key":"6118_CR2","doi-asserted-by":"crossref","unstructured":"G\u00fcm\u00fcsl\u00fc E, Erol Barkana D, K\u00f6se H (2020) October. Emotion recognition using EEG and physiological data for robot-assisted rehabilitation systems. In: Companion publication of the 2020 international conference on multimodal interaction, pp 379\u2013387","DOI":"10.1145\/3395035.3425199"},{"volume-title":"Deep learning techniques for biomedical and health informatics","year":"2020","key":"6118_CR3","unstructured":"Dash S, Acharya BR, Mittal M, Abraham A, Kelemen AG (eds) (2020) Deep learning techniques for biomedical and health informatics. Springer, Cham"},{"key":"6118_CR4","doi-asserted-by":"publisher","first-page":"103671","DOI":"10.1016\/j.compbiomed.2020.103671","volume":"119","author":"S Yang","year":"2020","unstructured":"Yang S, Li B, Zhang Y, Duan M, Liu S, Zhang Y, Feng X, Tan R, Huang L, Zhou F (2020) Selection of features for patient-independent detection of seizure events using scalp EEG signals. Comput Biol Med 119:103671","journal-title":"Comput Biol Med"},{"key":"6118_CR5","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2020.3043802","author":"MA Jan","year":"2020","unstructured":"Jan MA, Khan F, Khan R, Mastorakis S, Menon VG, Watters P, Alazab M (2020) A Lightweight Mutual Authentication and Privacy-preservation Scheme for Intelligent Wearable Devices in Industrial-CPS. IEEE Trans Industr Inf. https:\/\/doi.org\/10.1109\/TII.2020.3043802","journal-title":"IEEE Trans Industr Inf"},{"key":"6118_CR6","first-page":"14","volume":"07","author":"L Jiancheng","year":"1999","unstructured":"Jiancheng L, Zhanyu C (1999) prediction of EEG signal based on neural network. Appl Electron Technol 07:14\u201315","journal-title":"Appl Electron Technol"},{"key":"6118_CR7","unstructured":"Mao X, Meng Q (2005) EEG classification based on wavelet transform and neural network. J Beijing Univ Aeron Astron"},{"key":"6118_CR8","unstructured":"Cui Z (2019) Research on EEG signal analysis method based on convolution neural network"},{"issue":"10","key":"6118_CR9","first-page":"54","volume":"568","author":"F Guohang","year":"2019","unstructured":"Guohang F, Bingmei S, Boyi W et al (2019) Application of artificial neural network in EEG signal processing. Electron world 568(10):54\u201355","journal-title":"Electron world"},{"key":"6118_CR10","unstructured":"Xungung M (2020) Recognition of multi class EEG signals based on convolutional neural network"},{"key":"6118_CR11","unstructured":"Hongyu L, Qingjiang L, Xiaojuan C et al. (2019) Classification of EEG signals based on adaptive cascade neural network. Ind Innov Res"},{"issue":"022","key":"6118_CR12","first-page":"99","volume":"055","author":"T Li","year":"2019","unstructured":"Li T, Zhong ZJ, Jian Z et al (2019) Emotion recognition of EEG signals based on improved convolutional neural network. Comput Eng Appl 055(022):99\u2013105","journal-title":"Comput Eng Appl"},{"issue":"004","key":"6118_CR13","first-page":"589","volume":"036","author":"W Hongtao","year":"2019","unstructured":"Hongtao W, Hui H, Yuebang H et al (2019) Analysis method of event-related potential EEG signal based on noise reduction self-coding neural network. Control Theory Application 036(004):589\u2013595","journal-title":"Control Theory Application"},{"issue":"937","key":"6118_CR14","first-page":"109","volume":"55","author":"C Jingxia","year":"2019","unstructured":"Jingxia C, Liyan W, Xiaoyun J et al (2019) Emotion recognition of EEG signals based on deep convolution neural network. Comput Eng Appl 55(937):109\u2013116","journal-title":"Comput Eng Appl"},{"key":"6118_CR15","unstructured":"Yuetian L (2019) Application of convolutional neural network based on large interval softmax loss function in EEG signal classification"},{"issue":"004","key":"6118_CR16","first-page":"1041","volume":"042","author":"T Xianlun","year":"2020","unstructured":"Xianlun T, Wei Li, Weichang Ma et al (2020) EEG recognition based on conditional empirical mode decomposition and serial parallel CNN. Acta Electron Sin 042(004):1041\u20131048","journal-title":"Acta Electron Sin"},{"issue":"4","key":"6118_CR17","doi-asserted-by":"publisher","first-page":"1084","DOI":"10.1016\/j.eswa.2006.02.005","volume":"32","author":"A Subasi","year":"2007","unstructured":"Subasi A (2007) EEG signal classification using wavelet feature extraction and a mixture of expert model. Expert Syst Appl 32(4):1084\u20131093","journal-title":"Expert Syst Appl"},{"issue":"1","key":"6118_CR18","first-page":"63","volume":"9","author":"X Jiang","year":"2019","unstructured":"Jiang X, Wei S, Zhang S, Zhao L, Liu F, Tang H, He Z, Liu C (2019) Comparison of photoplethysmographic signal features between healthy and sleep apnea patients during five sleep stages. J Med Imag Health Inf 9(1):63\u201369","journal-title":"J Med Imag Health Inf"},{"issue":"9","key":"6118_CR19","first-page":"1757","volume":"8","author":"J Zhang","year":"2018","unstructured":"Zhang J, Yang L, Su Z, Mao X, Luo K, Liu C (2018) Photoplethysmogram signal quality assessment using support vector machine and multi-feature fusion. J Med Imag Health Inf 8(9):1757\u20131762","journal-title":"J Med Imag Health Inf"},{"issue":"7","key":"6118_CR20","first-page":"1368","volume":"8","author":"F Liu","year":"2018","unstructured":"Liu F, Liu C, Zhao L, Zhang X, Wu X, Xu X, Liu Y, Ma C, Wei S, He Z (2018) An open access database for evaluating the algorithms of electrocardiogram rhythm and morphology abnormality detection. J Med Imag Health Inf 8(7):1368\u20131373","journal-title":"J Med Imag Health Inf"},{"issue":"7","key":"6118_CR21","doi-asserted-by":"publisher","first-page":"1502","DOI":"10.1166\/jmihi.2018.2462","volume":"8","author":"Y Liu","year":"2018","unstructured":"Liu Y, Wei S, Zhang S, Liu F, Li J, Liu C (2018) Signal quality index based two-step method for heart rate estimation by combining electrocardiogram and arterial blood pressure signals. Journal of Medical Imaging and Health Informatics 8(7):1502\u20131507","journal-title":"Journal of Medical Imaging and Health Informatics"},{"issue":"7","key":"6118_CR22","first-page":"1486","volume":"8","author":"S Lu","year":"2018","unstructured":"Lu S, Lu Z, Phillips P, Wang S, Zhang Y (2018) Pathological brain detection in magnetic resonance imaging using combined features and improved extreme learning machines. J Med Imag Health Inf 8(7):1486\u20131490","journal-title":"J Med Imag Health Inf"},{"key":"6118_CR23","doi-asserted-by":"crossref","unstructured":"Zhao Z, Liu C, Li Y, Li Y, Wang J, Lin BS, and Li J (2019) Noise rejection for wearable ECGs using modified frequency slice wavelet transform and convolution neural networks. In: IEEE Access","DOI":"10.22489\/CinC.2019.138"},{"key":"6118_CR24","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2021.3069629","author":"Yu Xu","year":"2021","unstructured":"Xu Yu, Zhan D, Liu L, Lv H, Lingwei Xu, Junwei Du (2021) A privacy-preserving cross-domain healthcare wearables recommendation algorithm based on domain-dependent and domain-independent feature fusion. IEEE J Biomed Health Inform. https:\/\/doi.org\/10.1109\/JBHI.2021.3069629","journal-title":"IEEE J Biomed Health Inform"},{"key":"6118_CR25","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.knosys.2017.11.010","volume":"141","author":"Yu Xu","year":"2018","unstructured":"Xu Yu, Chu Y, Jiang F, Guo Y, Gong D (2018) SVMs Classification based two-side cross domain collaborative filtering by inferring intrinsic user and item features. Knowl Based Syst 141:80\u201391","journal-title":"Knowl Based Syst"},{"issue":"3","key":"6118_CR26","doi-asserted-by":"publisher","first-page":"767","DOI":"10.1016\/j.cmpb.2013.12.020","volume":"113","author":"Y Li","year":"2014","unstructured":"Li Y, Wen PP (2014) Modified CC-LR algorithm with three diverse feature sets for motor imagery tasks classification in EEG based brain-computer interface. Comput Methods Prog Biomed 113(3):767\u2013780","journal-title":"Comput Methods Prog Biomed"},{"key":"6118_CR27","doi-asserted-by":"crossref","unstructured":"Ashok A, Bharathan AK, Soujya V, Nandakumar P (2013) Tikhonov regularized spectrally weighted common spatial patterns. In 2013 International conference on control communication and computing (ICCC), IEEE, pp 315\u2013318","DOI":"10.1109\/ICCC.2013.6731671"},{"key":"6118_CR28","doi-asserted-by":"publisher","first-page":"39","DOI":"10.3389\/fnins.2012.00039","volume":"6","author":"KK Ang","year":"2012","unstructured":"Ang KK, Chin ZY, Wang C, Guan C, Zhang H (2012) Filter bank common spatial pattern algorithm on BCI competition IV datasets 2a and 2b. Front Neurosci 6:39","journal-title":"Front Neurosci"},{"issue":"1","key":"6118_CR29","doi-asserted-by":"publisher","first-page":"016003","DOI":"10.1088\/1741-2560\/14\/1\/016003","volume":"14","author":"YR Tabar","year":"2017","unstructured":"Tabar YR, Halici U (Feb 2017) A novel deep learning approach for classification of EEG motor imagery signals. J Neural Eng 14(1):016003","journal-title":"J Neural Eng"},{"key":"6118_CR30","doi-asserted-by":"crossref","unstructured":"Lee HK, Choi Y-S, (2018) A convolution neural networks scheme for classification of motor imagery EEG based on wavelet time-frequecy image. In 2018 International conference on information networking (ICOIN), IEEE, pp 906\u2013909","DOI":"10.1109\/ICOIN.2018.8343254"},{"key":"6118_CR31","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1016\/j.neulet.2013.08.021","volume":"553","author":"N Naseer","year":"2013","unstructured":"Naseer N, Hong K-S (2013) Classification of functional near-infrared spectroscopy signals corresponding to the right-and left-wrist motor imagery for development of a brain-computer interface. Neurosci Lett 553:84\u201389","journal-title":"Neurosci Lett"},{"issue":"4","key":"6118_CR32","doi-asserted-by":"publisher","first-page":"526","DOI":"10.1109\/TNSRE.2012.2184838","volume":"20","author":"S Siuly","year":"2012","unstructured":"Siuly S, Li Y (2012) Improving the separability of motor imagery EEG signals using a cross correlation-based least square support vector machine for brain-computer interface. IEEE Trans Neural Syst Rehabil Eng 20(4):526\u2013538","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"6118_CR33","unstructured":"Yang H, Sakhavi S, Ang KK, Guan C (2015) On the use of convolutional neural networks and augmented CSP features for multi-class motor imagery of EEG signals classification. In 2015 37th Annual international conference of the ieee engineering in medicine and biology society (EMBC), IEEE, pp 2620\u20132623"},{"key":"6118_CR34","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-021-05933-8","author":"Yu Miao","year":"2021","unstructured":"Miao Yu, Quan T, Qinglong Peng XuYu, Liu L (2021) A model-based collaborate filtering algorithm based on stacked autoencoder. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-021-05933-8","journal-title":"Neural Comput Appl"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06118-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-06118-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06118-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,2]],"date-time":"2023-06-02T09:52:37Z","timestamp":1685699557000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-06118-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,25]]},"references-count":34,"journal-issue":{"issue":"20","published-print":{"date-parts":[[2023,7]]}},"alternative-id":["6118"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-06118-z","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2021,9,25]]},"assertion":[{"value":"29 January 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 May 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 September 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors declare that all authors have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}