{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T04:36:17Z","timestamp":1776314177713,"version":"3.50.1"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2022,8,19]],"date-time":"2022-08-19T00:00:00Z","timestamp":1660867200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,8,19]],"date-time":"2022-08-19T00:00:00Z","timestamp":1660867200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation (NSFC) of China","doi-asserted-by":"crossref","award":["51775325"],"award-info":[{"award-number":["51775325"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Young Eastern Scholars Program of Shanghai","award":["QD2016033"],"award-info":[{"award-number":["QD2016033"]}]},{"name":"Hong Kong Scholars Program of China","award":["XJ2013015"],"award-info":[{"award-number":["XJ2013015"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Intell Manuf"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1007\/s10845-022-01997-y","type":"journal-article","created":{"date-parts":[[2022,8,19]],"date-time":"2022-08-19T07:03:07Z","timestamp":1660892587000},"page":"3179-3196","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Extracting and supplementing method for EEG signal in manufacturing workshop based on deep learning of time\u2013frequency correlation"],"prefix":"10.1007","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2551-4839","authenticated-orcid":false,"given":"Bin","family":"Ren","sequence":"first","affiliation":[]},{"given":"Yunjie","family":"Pan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,8,19]]},"reference":[{"issue":"6","key":"1997_CR1","doi-asserted-by":"publisher","first-page":"1466","DOI":"10.1109\/TII.2015.2491267","volume":"11","author":"TM Chiwewe","year":"2015","unstructured":"Chiwewe, T. M., Mbuya, C. F., & Hancke, G. P. (2015). Using cognitive radio for interference-resistant industrial wireless sensor networks: An overview. IEEE Transactions on Industrial Informatics, 11(6), 1466\u20131481. https:\/\/doi.org\/10.1109\/TII.2015.2491267","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"1997_CR2","doi-asserted-by":"crossref","unstructured":"Cho, K., Merriecboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation.\narXiv:1406.1078","DOI":"10.3115\/v1\/D14-1179"},{"key":"1997_CR3","unstructured":"Clevert, D. A., Unterthiner, T., & Hochreiter, S. (2015). Fast and accurate deep network learning by exponential linear units (elus). arXiv:1511.07289"},{"issue":"5","key":"1997_CR4","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/51.956815","volume":"20","author":"PB Colditz","year":"2001","unstructured":"Colditz, P. B., Burke, C. J., & Celka, P. (2001). Digital processing of EEG signals. IEEE Engineering in Medicine and Biology Magazine, 20(5), 21\u201322. https:\/\/doi.org\/10.1109\/51.956815","journal-title":"IEEE Engineering in Medicine and Biology Magazine"},{"issue":"5","key":"1997_CR5","doi-asserted-by":"publisher","first-page":"984","DOI":"10.1111\/1365-2478.13070","volume":"69","author":"Y Gao","year":"2021","unstructured":"Gao, Y., Zhao, P., Li, G., & Li, H. (2021). Seismic noise attenuation by signal reconstruction: An unsupervised machine learning approach. Geophysical Prospecting, 69(5), 984\u20131002. https:\/\/doi.org\/10.1111\/1365-2478.13070","journal-title":"Geophysical Prospecting"},{"issue":"9","key":"1997_CR6","doi-asserted-by":"publisher","first-page":"2771","DOI":"10.1007\/s00521-017-2875-1","volume":"30","author":"A Gholami","year":"2018","unstructured":"Gholami, A., Bonakdari, H., Zaji, A. H., Fenjan, S. A., & Akhtari, A. A. (2018). New radial basis function network method based on decision trees to predict flow variables in a curved channel. Neural Computing and Applications, 30(9), 2771\u20132785. https:\/\/doi.org\/10.1007\/s00521-017-2875-1","journal-title":"Neural Computing and Applications"},{"issue":"2","key":"1997_CR7","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1093\/scan\/nsx143","volume":"13","author":"LR Gianotti","year":"2018","unstructured":"Gianotti, L. R., Lobmaier, J. S., Calluso, C., Dahinden, F. M., & Knoch, D. (2018). Theta resting EEG in TPJ\/pSTS is associated with individual differences in the feeling of being looked at. Social Cognitive and Affective Neuroscience, 13(2), 216\u2013223. https:\/\/doi.org\/10.1093\/scan\/nsx143","journal-title":"Social cognitive and affective neuroscience"},{"key":"1997_CR8","unstructured":"Graves, A. (2013). Generating sequences with recurrent neural networks. arXiv:1308.0850"},{"issue":"5","key":"1997_CR9","doi-asserted-by":"publisher","first-page":"855","DOI":"10.1109\/TPAMI.2008.137","volume":"31","author":"A Graves","year":"2009","unstructured":"Graves, A., Liwicki, M., Fern\u00e1ndez, S., Bertolami, R., Bunke, H., & Schmidhuber, J. (2009). A novel connectionist system for unconstrained handwriting recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(5), 855\u2013868. https:\/\/doi.org\/10.1109\/TPAMI.2008.137","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"10","key":"1997_CR10","doi-asserted-by":"publisher","first-page":"2222","DOI":"10.1109\/TNNLS.2016.2582924","volume":"28","author":"K Greff","year":"2016","unstructured":"Greff, K., Srivastava, R. K., Koutn\u00edk, J., Steunebrink, B. R., & Schmidhuber, J. (2016). LSTM: A search space odyssey. IEEE Transactions on Neural Networks and Learning Systems, 28(10), 2222\u20132232. https:\/\/doi.org\/10.1109\/TNNLS.2016.2582924","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"1997_CR11","unstructured":"Hartmann, K. G., Schirrmeister, R. T., & Ball, T. (2018). EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signals. arXiv:1806.01875"},{"key":"1997_CR12","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-84858-7","volume-title":"The elements of statistical learning: Data mining, inference, and prediction","author":"T Hastie","year":"2009","unstructured":"Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction. Springer."},{"key":"1997_CR13","doi-asserted-by":"crossref","unstructured":"Hersche, M., Rellstab, T., Schiavone, P. D., Cavigelli, L., Benini, L., & Rahimi, A. (2018). Fast and accurate multiclass inference for MI-BCIs using large multiscale temporal and spectral features. In 2018 26th European Signal Processing Conference (EUSIPCO). 3\u20137 Sept. 2018. pp. 1690\u20131694","DOI":"10.23919\/EUSIPCO.2018.8553378"},{"issue":"1","key":"1997_CR14","doi-asserted-by":"publisher","first-page":"41","DOI":"10.21873\/cgp.20063","volume":"15","author":"S Huang","year":"2018","unstructured":"Huang, S., Cai, N., Pacheco, P. P., Narrandes, S., Wang, Y., & Xu, W. (2018). Applications of support vector machine (SVM) learning in cancer genomics. Cancer Genomics-Proteomics, 15(1), 41\u201351. https:\/\/doi.org\/10.21873\/cgp.20063","journal-title":"Cancer Genomics-Proteomics"},{"key":"1997_CR15","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1016\/j.neuroimage.2017.06.030","volume":"159","author":"M Jas","year":"2017","unstructured":"Jas, M., Engemann, D. A., Bekhti, Y., Raimondo, F., & Gramfort, A. (2017). Autoreject: Automated artifact rejection for MEG and EEG data. Neuroimage, 159, 417\u2013429. https:\/\/doi.org\/10.1016\/j.neuroimage.2017.06.030","journal-title":"Neuroimage"},{"key":"1997_CR16","unstructured":"Jozefowicz, R., Zaremba, W., & Sutskever, I. (2015). An empirical exploration of recurrent network architectures. In International conference on machine learning. PMLR, pp. 2342\u20132350."},{"key":"1997_CR17","doi-asserted-by":"publisher","first-page":"103650","DOI":"10.1016\/j.engappai.2020.103650","volume":"92","author":"GN Kouziokas","year":"2020","unstructured":"Kouziokas, G. N. (2020). A new W-SVM kernel combining PSO-neural network transformed vector and Bayesian optimized SVM in GDP forecasting. Engineering Applications of Artificial Intelligence, 92, 103650. https:\/\/doi.org\/10.1016\/j.engappai.2020.103650","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"1997_CR18","doi-asserted-by":"publisher","first-page":"24978","DOI":"10.1109\/ACCESS.2019.2900696","volume":"7","author":"X Liu","year":"2019","unstructured":"Liu, X., Wei, X., Guo, L., Liu, Y., Song, Q., & Jamalipour, A. (2019). Turning the signal interference into benefits: Towards indoor self-powered visible light communication for IoT devices in industrial radio-hostile environments. IEEE Access: Practical Innovations, Open Solutions, 7, 24978\u201324989. https:\/\/doi.org\/10.1109\/ACCESS.2019.2900696","journal-title":"IEEE Access: Practical Innovations, Open Solutions"},{"issue":"2","key":"1997_CR19","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1109\/TBME.2010.2082539","volume":"58","author":"F Lotte","year":"2011","unstructured":"Lotte, F., & Guan, C. (2011). Regularizing common spatial patterns to improve BCI designs: Unified theory and new algorithms. IEEE Transactions on Biomedical Engineering, 58(2), 355\u2013362. https:\/\/doi.org\/10.1109\/TBME.2010.2082539","journal-title":"IEEE Transactions on biomedical Engineering"},{"issue":"8","key":"1997_CR20","doi-asserted-by":"publisher","first-page":"1569","DOI":"10.1016\/j.clinph.2011.01.050","volume":"122","author":"CF Lu","year":"2011","unstructured":"Lu, C. F., Teng, S., Hung, C. I., Tseng, P. J., Lin, L. T., Lee, P. L., et al. (2011). Reorganization of functional connectivity during the motor task using EEG time-frequency cross mutual information analysis. Clinical Neurophysiology, 122(8), 1569\u20131579. https:\/\/doi.org\/10.1016\/j.clinph.2011.01.050","journal-title":"Clinical Neurophysiology"},{"issue":"7","key":"1997_CR21","doi-asserted-by":"publisher","first-page":"1832","DOI":"10.1109\/TSP.2015.2507546","volume":"64","author":"AG Marques","year":"2015","unstructured":"Marques, A. G., Segarra, S., Leus, G., & Ribeiro, A. (2015). Sampling of graph signals with successive local aggregations. IEEE Transactions on Signal Processing, 64(7), 1832\u20131843. https:\/\/doi.org\/10.1109\/TSP.2015.2507546","journal-title":"IEEE Transactions on Signal Processing"},{"issue":"2","key":"1997_CR22","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1111\/j.1469-8986.2010.01061.x","volume":"48","author":"A Mognon","year":"2011","unstructured":"Mognon, A., Jovicich, J., Bruzzone, L., & Buiatti, M. (2011). ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features. Psychophysiology, 48(2), 229\u2013240. https:\/\/doi.org\/10.1111\/j.1469-8986.2010.01061.x","journal-title":"Psychophysiology"},{"issue":"1","key":"1997_CR23","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1016\/j.jneumeth.2010.07.015","volume":"192","author":"H Nolan","year":"2010","unstructured":"Nolan, H., Whelan, R., & Reilly, R. B. (2010). FASTER: Fully automated statistical thresholding for EEG artifact rejection. Journal of Neuroscience Methods, 192(1), 152\u2013162. https:\/\/doi.org\/10.1016\/j.jneumeth.2010.07.015","journal-title":"Journal of Neuroscience Methods"},{"key":"1997_CR24","doi-asserted-by":"publisher","unstructured":"Pfurtscheller, G., & Silva, F. H. L. (1999). Event related EEG \/MEG synchronization and desynchronization: Basic principles. Clinical Neurophysiology, 110(11), 1842\u20131857. https:\/\/doi.org\/10.1016\/S1388-2457(99)00141-8","DOI":"10.1016\/S1388-2457(99)00141-8"},{"key":"1997_CR25","doi-asserted-by":"crossref","unstructured":"Sakai, T., Shoji, T., Yoshida, N., Fukumori, K., Tanaka, Y., & Tanaka, T. (2020). SCALPNET: Detection of spatiotemporal abnormal intervals in epileptic EEG using convolutional neural networks. In ICASSP 2020\u20132020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp. 1244\u20131248.","DOI":"10.1109\/ICASSP40776.2020.9054705"},{"issue":"2","key":"1997_CR26","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1080\/15598608.2017.1353456","volume":"12","author":"A Sayed","year":"2018","unstructured":"Sayed, A., & Ibrahim, A. (2018). Recent developments in systematic sampling: A review. Journal of Statistical Theory and Practice, 12(2), 290\u2013310. https:\/\/doi.org\/10.1080\/15598608.2017.1353456","journal-title":"Journal of Statistical Theory and Practice"},{"issue":"3","key":"1997_CR27","doi-asserted-by":"publisher","first-page":"1061","DOI":"10.1109\/TIE.2010.2049711","volume":"58","author":"J Silvestre-Blanes","year":"2010","unstructured":"Silvestre-Blanes, J., Almeida, L., Marau, R., & Pedreiras, P. (2010). Online QoS management for multimedia real-time transmission in industrial networks. IEEE Transactions on Industrial Electronics, 58(3), 1061\u20131071. https:\/\/doi.org\/10.1109\/TIE.2010.2049711","journal-title":"IEEE Transactions on Industrial Electronics"},{"issue":"2","key":"1997_CR28","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1007\/s10916-008-9231-z","volume":"34","author":"DP Subha","year":"2010","unstructured":"Subha, D. P., Joseph, P. K., Acharya, R., & Lim, C. M. (2010). EEG signal analysis: A survey. Journal of medical systems, 34(2), 195\u2013212. https:\/\/doi.org\/10.1007\/s10916-008-9231-z","journal-title":"Journal of medical systems"},{"key":"1997_CR29","doi-asserted-by":"crossref","unstructured":"Sun, Y., Wang, X., & Tang, X. (2014). Deep learning face representation from predicting 10,000 classes. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1891\u20131898.","DOI":"10.1109\/CVPR.2014.244"},{"key":"1997_CR30","unstructured":"Sutskever, I., Vinyals, O., & Le, Q. V. (2014). Sequence to sequence learning with neural networks. Advances in neural information processing systems, p. 27."},{"key":"1997_CR31","doi-asserted-by":"publisher","first-page":"55","DOI":"10.3389\/fnins.2012.00055","volume":"6","author":"M Tangermann","year":"2012","unstructured":"Tangermann, M., M\u00fcller, K. R., Aertsen, A., Birbaumer, N., Braun, C., Brunner, C., et al. (2012). Review of the BCI competition IV. Frontiers in Neuroscience, 6, 55. https:\/\/doi.org\/10.3389\/fnins.2012.00055","journal-title":"Frontiers in neuroscience"},{"key":"1997_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neucom.2020.09.017","volume":"421","author":"Z Wan","year":"2021","unstructured":"Wan, Z., Yang, R., Huang, M., Zeng, N., & Liu, X. (2021). A review on transfer learning in EEG signal analysis. Neurocomputing, 421, 1\u201314. https:\/\/doi.org\/10.1016\/j.neucom.2020.09.017","journal-title":"Neurocomputing"},{"issue":"11","key":"1997_CR33","doi-asserted-by":"publisher","first-page":"8373","DOI":"10.1007\/s11227-019-03096-x","volume":"76","author":"Z Wang","year":"2020","unstructured":"Wang, Z., Horng, G., Hsu, T., Aripriharta, A., & Jong, G. (2020). Heart sound signal recovery based on time series signal prediction using a recurrent neural network in the long short-term memory model. The Journal of Supercomputing, 76(11), 8373\u20138390. https:\/\/doi.org\/10.1007\/s11227-019-03096-x","journal-title":"The Journal of Supercomputing"},{"key":"1997_CR34","doi-asserted-by":"crossref","unstructured":"Xue, H., Huynh, D. Q., & Reynolds, M. (2018). SS-LSTM: A hierarchical LSTM model for pedestrian trajectory prediction. In  2018 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, pp. 1186\u20131194.","DOI":"10.1109\/WACV.2018.00135"},{"key":"1997_CR35","doi-asserted-by":"publisher","first-page":"114513","DOI":"10.1016\/j.eswa.2020.114513","volume":"169","author":"R Yan","year":"2021","unstructured":"Yan, R., Liao, J., Yang, J., Sun, W., Nong, M., & Li, F. (2021). Multi-hour and multi-site air quality index forecasting in Beijing using CNN, LSTM, CNN-LSTM, and spatiotemporal clustering. Expert Systems with Applications, 169, 114513. https:\/\/doi.org\/10.1016\/j.eswa.2020.114513","journal-title":"Expert Systems with Applications"},{"issue":"21","key":"1997_CR36","doi-asserted-by":"publisher","first-page":"16011","DOI":"10.1007\/s00500-020-04920-w","volume":"24","author":"K Yasoda","year":"2020","unstructured":"Yasoda, K., Ponmagal, R., Bhuvaneshwari, K., & Venkatachalam, K. (2020). Automatic detection and classification of EEG artifacts using fuzzy kernel SVM and wavelet ICA (WICA). Soft Computing, 24(21), 16011\u201316019. https:\/\/doi.org\/10.1007\/s00500-020-04920-w","journal-title":"Soft Computing"},{"issue":"3","key":"1997_CR37","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1007\/s10661-019-7196-7","volume":"191","author":"M Zeinolabedini","year":"2019","unstructured":"Zeinolabedini, M., & Najafzadeh, M. (2019). Comparative study of different wavelet-based neural network models to predict sewage sludge quantity in wastewater treatment plant. Environmental Monitoring and Assessment, 191(3), 163. https:\/\/doi.org\/10.1007\/s10661-019-7196-7","journal-title":"Environmental Monitoring and Assessment"},{"key":"1997_CR38","doi-asserted-by":"crossref","unstructured":"Zhang, K., & Luo, Y. (2020). Effects of worker fatigue on assembly line balancing. In  2020 IEEE 11th international conference on software engineering and service science (ICSESS). IEEE, pp. 254\u2013257.","DOI":"10.1109\/ICSESS49938.2020.9237704"},{"key":"1997_CR39","doi-asserted-by":"publisher","first-page":"440","DOI":"10.1016\/j.physa.2016.01.056","volume":"451","author":"W Zhang","year":"2016","unstructured":"Zhang, W., Wei, Z., Wang, B., & Han, X. (2016). Measuring mixing patterns in complex networks by Spearman rank correlation coefficient. Physica A: Statistical Mechanics and its Applications, 451, 440\u2013450. https:\/\/doi.org\/10.1016\/j.physa.2016.01.056","journal-title":"Physica A: Statistical Mechanics and its Applications"},{"key":"1997_CR40","doi-asserted-by":"crossref","unstructured":"Zhang, Z. (2018). Improved adam optimizer for deep neural networks. In 2018 IEEE\/ACM 26th international symposium on quality of service (IWQoS). IEEE, pp. 1\u20132.","DOI":"10.1109\/IWQoS.2018.8624183"}],"container-title":["Journal of Intelligent Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-022-01997-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10845-022-01997-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-022-01997-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,29]],"date-time":"2023-07-29T12:09:28Z","timestamp":1690632568000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10845-022-01997-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,19]]},"references-count":40,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["1997"],"URL":"https:\/\/doi.org\/10.1007\/s10845-022-01997-y","relation":{},"ISSN":["0956-5515","1572-8145"],"issn-type":[{"value":"0956-5515","type":"print"},{"value":"1572-8145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,19]]},"assertion":[{"value":"18 August 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 July 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 August 2022","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 that there are no conflict of interests, we do not have any possible conflicts of interest. We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and\/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}