{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T08:26:22Z","timestamp":1768811182510,"version":"3.49.0"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,2,6]],"date-time":"2024-02-06T00:00:00Z","timestamp":1707177600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,2,6]],"date-time":"2024-02-06T00:00:00Z","timestamp":1707177600000},"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":["SIViP"],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1007\/s11760-023-02986-1","type":"journal-article","created":{"date-parts":[[2024,2,6]],"date-time":"2024-02-06T18:03:28Z","timestamp":1707242608000},"page":"3243-3254","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A novel precisely designed compact convolutional EEG classifier for motor imagery classification"],"prefix":"10.1007","volume":"18","author":[{"given":"Muhammad Ahmed","family":"Abbasi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hafza Faiza","family":"Abbasi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muhammad Zulkifal","family":"Aziz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Waseem","family":"Haider","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zeming","family":"Fan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaojun","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,2,6]]},"reference":[{"issue":"13","key":"2986_CR1","doi-asserted-by":"publisher","first-page":"6001","DOI":"10.3390\/s23136001","volume":"23","author":"J Peksa","year":"2023","unstructured":"Peksa, J., Mamchur, D.: State-of-the-art on brain\u2013computer interface technology. Sensors 23(13), 6001 (2023)","journal-title":"Sensors"},{"key":"2986_CR2","doi-asserted-by":"publisher","first-page":"46","DOI":"10.53759\/181X\/JCNS202303005","volume":"3","author":"Z Jiping","year":"2023","unstructured":"Jiping, Z.: Brain computer interface system, performance, challenges and applications. J. Comput. Nat. Sci 3, 46 (2023)","journal-title":"J. Comput. Nat. Sci"},{"key":"2986_CR3","unstructured":"ARI, M.: Brain-computer interfaces: exploring the convergence of medicine and technology p.\u00a024 (2023)"},{"key":"2986_CR4","doi-asserted-by":"publisher","first-page":"2845","DOI":"10.1109\/TNSRE.2022.3211276","volume":"30","author":"J Wang","year":"2022","unstructured":"Wang, J., et al.: EEG-based continuous hand movement decoding using improved center-out paradigm. IEEE Trans. Neural Syst. Rehabilitat. Eng. 30, 2845 (2022)","journal-title":"IEEE Trans. Neural Syst. Rehabilitat. Eng."},{"issue":"3","key":"2986_CR5","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ac74e0","volume":"19","author":"P Arpaia","year":"2022","unstructured":"Arpaia, P., et al.: How to successfully classify EEG in motor imagery BCI: a metrological analysis of the state of the art. J. Neural Eng. 19(3), 031002 (2022)","journal-title":"J. Neural Eng."},{"key":"2986_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.103843","volume":"123","author":"M Khan","year":"2020","unstructured":"Khan, M., et al.: Review on motor imagery based BCI systems for upper limb post-stroke neurorehabilitation: from designing to application. Comput. Biol. Med. 123, 103843 (2020)","journal-title":"Comput. Biol. Med."},{"key":"2986_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.104079","volume":"127","author":"J Choi","year":"2020","unstructured":"Choi, J., Huh, S., Jo, S.: Improving performance in motor imagery BCI-based control applications via virtually embodied feedback. Comput. Biol. Med. 127, 104079 (2020)","journal-title":"Comput. Biol. Med."},{"key":"2986_CR8","doi-asserted-by":"crossref","unstructured":"Teo, W.P., White, D., Macpherson, H.: Using noninvasive methods to drive brain\u2013computer interface (BCI): the role of electroencephalography and functional near-infrared spectroscopy in BCI. in Smart Wheelchairs and Brain-Computer Interfaces (Elsevier, 2018), pp. 33\u201363","DOI":"10.1016\/B978-0-12-812892-3.00003-0"},{"key":"2986_CR9","doi-asserted-by":"publisher","first-page":"324","DOI":"10.37394\/23203.2022.17.37","volume":"17","author":"C Murthy","year":"2022","unstructured":"Murthy, C., Sridevi, K.: Design and implementation of hybrid techniques and DA-based reconfigurable FIR filter design for noise removal in EEG signals on FPGA. WSEAS Trans. Syst. Cont. 17, 324 (2022)","journal-title":"WSEAS Trans. Syst. Cont."},{"key":"2986_CR10","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1016\/j.neucom.2019.02.060","volume":"347","author":"S Kanoga","year":"2019","unstructured":"Kanoga, S., Kanemura, A., Asoh, H.: Multi-scale dictionary learning for ocular artifact reduction from single-channel electroencephalograms. Neurocomputing 347, 240 (2019)","journal-title":"Neurocomputing"},{"key":"2986_CR11","doi-asserted-by":"crossref","unstructured":"Madduri, V., et\u00a0al.: A review of methods for suppression of muscle artifacts in scalp EEG signals. in AIP Conference Proceedings (AIP Publishing, 2023)","DOI":"10.1063\/5.0148898"},{"issue":"14","key":"2986_CR12","doi-asserted-by":"publisher","first-page":"5353","DOI":"10.1109\/JSEN.2019.2906572","volume":"19","author":"X Chen","year":"2019","unstructured":"Chen, X., et al.: Removal of muscle artifacts from the EEG: a review and recommendations. IEEE Sens. J. 19(14), 5353 (2019)","journal-title":"IEEE Sens. J."},{"key":"2986_CR13","doi-asserted-by":"crossref","unstructured":"Yong, X., Ward, R., Birch, G.: Generalized morphological component analysis for EEG source separation and artifact removal. in 2009 4th International IEEE\/EMBS Conference on Neural Engineering (IEEE, 2009)","DOI":"10.1109\/NER.2009.5109303"},{"issue":"25","key":"2986_CR14","doi-asserted-by":"publisher","first-page":"1367","DOI":"10.1049\/el.2020.2509","volume":"56","author":"M Sadiq","year":"2020","unstructured":"Sadiq, M., et al.: Motor imagery BCI classification based on novel two dimensional modelling in empirical wavelet transform. Electron. Lett. 56(25), 1367 (2020)","journal-title":"Electron. Lett."},{"key":"2986_CR15","first-page":"1","volume":"70","author":"X Yu","year":"2021","unstructured":"Yu, X., et al.: A new framework for automatic detection of motor and mental imagery EEG signals for robust BCI systems. IEEE Trans. Instrument. Measure. 70, 1 (2021)","journal-title":"IEEE Trans. Instrument. Measure."},{"key":"2986_CR16","doi-asserted-by":"crossref","unstructured":"Shovon, T., et\u00a0al.: Classification of motor imagery EEG signals with multi-input convolutional neural network by augmenting STFT. in 2019 5th International Conference on Advances in Electrical Engineering (ICAEE) (IEEE, 2019)","DOI":"10.1109\/ICAEE48663.2019.8975578"},{"issue":"4","key":"2986_CR17","doi-asserted-by":"publisher","first-page":"1006","DOI":"10.1109\/TNSRE.2020.2979464","volume":"28","author":"A Jiang","year":"2020","unstructured":"Jiang, A., et al.: Efficient CSP algorithm with spatio-temporal filtering for motor imagery classification. IEEE Trans. Neural Syst. Rehabilitat. Eng. 28(4), 1006 (2020)","journal-title":"IEEE Trans. Neural Syst. Rehabilitat. Eng."},{"key":"2986_CR18","doi-asserted-by":"crossref","unstructured":"Das, R., et\u00a0al.: FBCSP and adaptive boosting for multiclass motor imagery BCI data classification: a machine learning approach. in 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (IEEE, 2020)","DOI":"10.1109\/SMC42975.2020.9283098"},{"issue":"2","key":"2986_CR19","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1109\/TCDS.2020.3040438","volume":"14","author":"M Sadiq","year":"2020","unstructured":"Sadiq, M., et al.: A matrix determinant feature extraction approach for decoding motor and mental imagery EEG in subject-specific tasks. IEEE Trans. Cognit. Develop. Syst. 14(2), 375 (2020)","journal-title":"IEEE Trans. Cognit. Develop. Syst."},{"key":"2986_CR20","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2020.587520","volume":"14","author":"X Liu","year":"2020","unstructured":"Liu, X., et al.: Parallel spatial temporal self-attention CNN-based motor imagery classification for BCI. Front. Neurosci. 14, 587520 (2020)","journal-title":"Front. Neurosci."},{"key":"2986_CR21","volume":"9","author":"J Leoni","year":"2022","unstructured":"Leoni, J., et al.: State-of-the-art on brain\u2013computer interface technology. Machine Learn. Appl. 9, 100393 (2022)","journal-title":"Machine Learn. Appl."},{"key":"2986_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.103342","volume":"72","author":"H Li","year":"2022","unstructured":"Li, H., et al.: Motor imagery EEG classification algorithm based on CNN-LSTM feature fusion network. Biomed signal Proc. Cont. 72, 103342 (2022)","journal-title":"Biomed signal Proc. Cont."},{"key":"2986_CR23","first-page":"1","volume":"60","author":"Y Feng","year":"2022","unstructured":"Feng, Y., et al.: ICIF-Net: Intra-scale cross-interaction and inter-scale feature fusion network for bitemporal remote sensing images change detection. IEEE Trans. Geosci. Remote Sens. 60, 1 (2022)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"11","key":"2986_CR24","doi-asserted-by":"publisher","first-page":"5391","DOI":"10.1002\/hbm.23730","volume":"38","author":"R Schirrmeister","year":"2017","unstructured":"Schirrmeister, R., et al.: Deep learning with convolutional neural networks for EEG decoding and visualization. Human Brain Mapping 38(11), 5391 (2017)","journal-title":"Human Brain Mapping"},{"issue":"5","key":"2986_CR25","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/aace8c","volume":"15","author":"V Lawhern","year":"2018","unstructured":"Lawhern, V., et al.: EEGNet: a compact convolutional neural network for EEG-based brain\u2013computer interfaces. J. Neural Eng. 15(5), 056013 (2018)","journal-title":"J. Neural Eng."},{"issue":"1","key":"2986_CR26","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ab405f","volume":"17","author":"G Dai","year":"2020","unstructured":"Dai, G., et al.: HS-CNN: a CNN with hybrid convolution scale for EEG motor imagery classification. J. Neural Eng. 17(1), 016025 (2020)","journal-title":"J. Neural Eng."},{"key":"2986_CR27","doi-asserted-by":"crossref","unstructured":"Barmpas, K., et\u00a0al.: BrainWave-Scattering Net: A lightweight network for EEG-based motor imagery recognition. J. Neural Eng. (2023)","DOI":"10.1088\/1741-2552\/acf78a"},{"issue":"4","key":"2986_CR28","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1109\/MC.2012.107","volume":"45","author":"J Van Erp","year":"2012","unstructured":"Van Erp, J., Lotte, F., Tangermann, M.: Brain-computer interfaces: beyond medical applications. Computer 45(4), 26 (2012)","journal-title":"Computer"},{"issue":"12","key":"2986_CR29","doi-asserted-by":"publisher","first-page":"2677","DOI":"10.3390\/sym14122677","volume":"14","author":"B Huang","year":"2022","unstructured":"Huang, B., et al.: Exploiting asymmetric EEG signals with EFD in deep learning domain for robust BCI. Symmetry 14(12), 2677 (2022)","journal-title":"Symmetry"},{"issue":"4","key":"2986_CR30","doi-asserted-by":"publisher","first-page":"1600","DOI":"10.1016\/j.neuroimage.2006.09.024","volume":"34","author":"V Jurcak","year":"2007","unstructured":"Jurcak, V., Tsuzuki, D., Dan, I.: 10\/20, 10\/10, and 10\/5 systems revisited: their validity as relative head-surface-based positioning systems. Neuroimage 34(4), 1600 (2007)","journal-title":"Neuroimage"},{"key":"2986_CR31","unstructured":"Kipf, T., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)"},{"key":"2986_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105242","volume":"143","author":"M Sadiq","year":"2022","unstructured":"Sadiq, M., et al.: Exploiting pretrained CNN models for the development of an EEG-based robust BCI framework. Comput. Biol. Med. 143, 105242 (2022)","journal-title":"Comput. Biol. Med."},{"issue":"5","key":"2986_CR33","doi-asserted-by":"publisher","first-page":"1177","DOI":"10.1109\/TETCI.2022.3147030","volume":"6","author":"M Sadiq","year":"2022","unstructured":"Sadiq, M., et al.: Motor imagery BCI classification based on multivariate variational mode decomposition. IEEE Trans. Emerg. Top. Comput. Intell. 6(5), 1177 (2022)","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"2986_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neunet.2020.12.013","volume":"136","author":"K Zhang","year":"2021","unstructured":"Zhang, K., et al.: Adaptive transfer learning for EEG motor imagery classification with deep convolutional neural network. Neural Netw. 136, 1 (2021)","journal-title":"Neural Netw."},{"key":"2986_CR35","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1007\/s11222-009-9153-8","volume":"21","author":"T Fushiki","year":"2011","unstructured":"Fushiki, T.: Estimation of prediction error by using K-fold cross-validation. Statist. Comput. 21, 137 (2011)","journal-title":"Statist. Comput."},{"issue":"12","key":"2986_CR36","doi-asserted-by":"publisher","first-page":"2773","DOI":"10.1109\/TNSRE.2020.3048106","volume":"28","author":"E Santamaria-Vazquez","year":"2020","unstructured":"Santamaria-Vazquez, E., et al.: EEG-inception: a novel deep convolutional neural network for assistive ERP-based brain-computer interfaces. IEEE Trans. Neural Syst. Rehabilitat. Eng. 28(12), 2773 (2020)","journal-title":"IEEE Trans. Neural Syst. Rehabilitat. Eng."},{"key":"2986_CR37","doi-asserted-by":"crossref","unstructured":"Miao, M., et\u00a0al.: Spatial-frequency feature learning and classification of motor imagery EEG based on deep convolution neural network. Comput. Math. Methods Med (2020)","DOI":"10.1155\/2020\/1981728"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-023-02986-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-023-02986-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-023-02986-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,25]],"date-time":"2024-03-25T16:08:11Z","timestamp":1711382891000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-023-02986-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,6]]},"references-count":37,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,6]]}},"alternative-id":["2986"],"URL":"https:\/\/doi.org\/10.1007\/s11760-023-02986-1","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,6]]},"assertion":[{"value":"27 October 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 November 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 December 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 February 2024","order":4,"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 no potential conflict of interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interests"}},{"value":"Ethical approval was not sought for the present study because the datasets utilized in this study are publicly available.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}}]}}