{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T19:00:00Z","timestamp":1783710000579,"version":"3.55.0"},"publisher-location":"Singapore","reference-count":29,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819981373","type":"print"},{"value":"9789819981380","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T00:00:00Z","timestamp":1700956800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T00:00:00Z","timestamp":1700956800000},"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":[[2024]]},"DOI":"10.1007\/978-981-99-8138-0_26","type":"book-chapter","created":{"date-parts":[[2023,11,25]],"date-time":"2023-11-25T10:02:23Z","timestamp":1700906543000},"page":"326-337","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Enhanced Motor Imagery Based Brain-Computer Interface via\u00a0Vibration Stimulation and\u00a0Robotic Glove for\u00a0Post-Stroke Rehabilitation"],"prefix":"10.1007","author":[{"given":"Jianqiang","family":"Su","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaxing","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiqun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yihan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zeng-Guang","family":"Hou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,11,26]]},"reference":[{"key":"26_CR1","doi-asserted-by":"publisher","first-page":"e8832686","DOI":"10.1155\/2021\/8832686","volume":"2021","author":"D Achanccaray","year":"2021","unstructured":"Achanccaray, D., Izumi, S.I., Hayashibe, M.: Visual-electrotactile stimulation feedback to improve immersive brain-computer interface based on hand motor imagery. Comput. Intell. Neurosci. 2021, e8832686 (2021). https:\/\/doi.org\/10.1155\/2021\/8832686","journal-title":"Comput. Intell. Neurosci."},{"issue":"1","key":"26_CR2","doi-asserted-by":"publisher","first-page":"2421","DOI":"10.1038\/s41467-018-04673-z","volume":"9","author":"A Biasiucci","year":"2018","unstructured":"Biasiucci, A., et al.: Brain-actuated functional electrical stimulation elicits lasting arm motor recovery after stroke. Nat. Commun. 9(1), 2421 (2018). https:\/\/doi.org\/10.1038\/s41467-018-04673-z","journal-title":"Nat. Commun."},{"issue":"4","key":"26_CR3","doi-asserted-by":"publisher","first-page":"2194","DOI":"10.1109\/TRO.2022.3148909","volume":"38","author":"W Chen","year":"2022","unstructured":"Chen, W., et al.: Soft exoskeleton with fully actuated thumb movements for grasping assistance. IEEE Trans. Rob. 38(4), 2194\u20132207 (2022). https:\/\/doi.org\/10.1109\/TRO.2022.3148909","journal-title":"IEEE Trans. Rob."},{"issue":"12","key":"26_CR4","doi-asserted-by":"publisher","first-page":"3339","DOI":"10.1109\/TBME.2020.2984003","volume":"67","author":"N Cheng","year":"2020","unstructured":"Cheng, N., et al.: Brain-computer interface-based soft robotic glove rehabilitation for stroke. IEEE Trans. Biomed. Eng. 67(12), 3339\u20133351 (2020)","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"12","key":"26_CR5","doi-asserted-by":"publisher","first-page":"3339","DOI":"10.1109\/TBME.2020.2984003","volume":"67","author":"N Cheng","year":"2020","unstructured":"Cheng, N., et al.: Brain-computer interface-based soft robotic glove rehabilitation for stroke. IEEE Trans. Biomed. Eng. 67(12), 3339\u20133351 (2020). https:\/\/doi.org\/10.1109\/TBME.2020.2984003","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"26_CR6","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.compbiomed.2018.09.021","volume":"103","author":"AP Costa","year":"2018","unstructured":"Costa, A.P., M\u00f8ller, J.S., Iversen, H.K., Puthusserypady, S.: An adaptive CSP filter to investigate user independence in a 3-class MI-BCI paradigm. Comput. Biol. Med. 103, 24\u201333 (2018)","journal-title":"Comput. Biol. Med."},{"issue":"5","key":"26_CR7","doi-asserted-by":"publisher","first-page":"056013","DOI":"10.1088\/1741-2560\/12\/5\/056013","volume":"12","author":"M Jochumsen","year":"2015","unstructured":"Jochumsen, M., Niazi, I.K., Taylor, D., Farina, D., Dremstrup, K.: Detecting and classifying movement-related cortical potentials associated with hand movements in healthy subjects and stroke patients from single-electrode, single-trial EEG. J. Neural Eng. 12(5), 056013 (2015)","journal-title":"J. Neural Eng."},{"issue":"5","key":"26_CR8","doi-asserted-by":"publisher","first-page":"056013","DOI":"10.1088\/1741-2552\/aace8c","volume":"15","author":"VJ Lawhern","year":"2018","unstructured":"Lawhern, V.J., Solon, A.J., Waytowich, N.R., Gordon, S.M., Hung, C.P., Lance, B.J.: EEGNet: a compact convolutional neural network for EEG-based brain\u2013computer interfaces. J. Neural Eng. 15(5), 056013 (2018). https:\/\/doi.org\/10.1088\/1741-2552\/aace8c","journal-title":"J. Neural Eng."},{"key":"26_CR9","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.cmpb.2016.04.023","volume":"132","author":"S Liang","year":"2016","unstructured":"Liang, S., Choi, K.S., Qin, J., Pang, W.M., Wang, Q., Heng, P.A.: Improving the discrimination of hand motor imagery via virtual reality based visual guidance. Comput. Methods Programs Biomed. 132, 63\u201374 (2016). https:\/\/doi.org\/10.1016\/j.cmpb.2016.04.023","journal-title":"Comput. Methods Programs Biomed."},{"key":"26_CR10","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1007\/978-3-030-92310-5_46","volume-title":"Neural Information Processing","author":"B Lu","year":"2021","unstructured":"Lu, B., Ge, S., Wang, H.: EEG-based classification of\u00a0lower limb motor imagery with\u00a0STFT and\u00a0CNN. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds.) ICONIP 2021. CCIS, vol. 1517, pp. 397\u2013404. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-92310-5_46"},{"issue":"6","key":"26_CR11","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1136\/svn-2022-001506","volume":"7","author":"R Mane","year":"2022","unstructured":"Mane, R., Wu, Z., Wang, D.: Poststroke motor, cognitive and speech rehabilitation with brain-computer interface: A perspective review. Stroke Vasc. Neurol. 7(6), 541\u2013549 (2022). https:\/\/doi.org\/10.1136\/svn-2022-001506","journal-title":"Stroke Vasc. Neurol."},{"key":"26_CR12","doi-asserted-by":"publisher","unstructured":"Miao, M., Zeng, H., Wang, A.: Composite and multiple kernel learning for brain computer interface. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds.) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science, vol. 10635, pp. 803\u2013810. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-70096-0_82","DOI":"10.1007\/978-3-319-70096-0_82"},{"issue":"2","key":"26_CR13","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1177\/15459683211062895","volume":"36","author":"I Nojima","year":"2022","unstructured":"Nojima, I., Sugata, H., Takeuchi, H., Mima, T.: Brain\u2013computer interface training based on brain activity can induce motor recovery in patients with stroke: A meta-analysis. Neurorehabil. Neural Repair 36(2), 83\u201396 (2022). https:\/\/doi.org\/10.1177\/15459683211062895","journal-title":"Neurorehabil. Neural Repair"},{"issue":"8","key":"26_CR14","doi-asserted-by":"publisher","first-page":"e0182578","DOI":"10.1371\/journal.pone.0182578","volume":"12","author":"P Ofner","year":"2017","unstructured":"Ofner, P., Schwarz, A., Pereira, J., M\u00fcller-Putz, G.R.: Upper limb movements can be decoded from the time-domain of low-frequency EEG. PLoS ONE 12(8), e0182578 (2017). https:\/\/doi.org\/10.1371\/journal.pone.0182578","journal-title":"PLoS ONE"},{"issue":"1","key":"26_CR15","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1016\/j.neuroimage.2005.12.003","volume":"31","author":"G Pfurtscheller","year":"2006","unstructured":"Pfurtscheller, G., Brunner, C., Schl\u00f6gl, A., Da Silva, F.L.: Mu rhythm (de) synchronization and EEG single-trial classification of different motor imagery tasks. Neuroimage 31(1), 153\u2013159 (2006)","journal-title":"Neuroimage"},{"issue":"8","key":"26_CR16","doi-asserted-by":"publisher","first-page":"1846","DOI":"10.1109\/TNSRE.2020.3001990","volume":"28","author":"S Ren","year":"2020","unstructured":"Ren, S., Wang, W., Hou, Z.G., Liang, X., Wang, J., Shi, W.: Enhanced motor imagery based brain-computer interface via FES and VR for lower limbs. IEEE Trans. Neural Syst. Rehabil. Eng. 28(8), 1846\u20131855 (2020). https:\/\/doi.org\/10.1109\/TNSRE.2020.3001990","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"issue":"5218","key":"26_CR17","doi-asserted-by":"publisher","first-page":"1775","DOI":"10.1126\/science.7792606","volume":"268","author":"JN Sanes","year":"1995","unstructured":"Sanes, J.N., Donoghue, J.P., Thangaraj, V., Edelman, R.R., Warach, S.: Shared neural substrates controlling hand movements in human motor cortex. Science 268(5218), 1775\u20131777 (1995). https:\/\/doi.org\/10.1126\/science.7792606","journal-title":"Science"},{"issue":"3","key":"26_CR18","doi-asserted-by":"publisher","first-page":"036010","DOI":"10.1088\/1741-2552\/ab882e","volume":"17","author":"A Schwarz","year":"2020","unstructured":"Schwarz, A., H\u00f6ller, M.K., Pereira, J., Ofner, P., M\u00fcller-Putz, G.R.: Decoding hand movements from human EEG to control a robotic arm in a simulation environment. J. Neural Eng. 17(3), 036010 (2020). https:\/\/doi.org\/10.1088\/1741-2552\/ab882e","journal-title":"J. Neural Eng."},{"issue":"1","key":"26_CR19","doi-asserted-by":"publisher","first-page":"016005","DOI":"10.1088\/1741-2552\/aa8911","volume":"15","author":"A Schwarz","year":"2018","unstructured":"Schwarz, A., Ofner, P., Pereira, J., Sburlea, A.I., M\u00fcller-Putz, G.R.: Decoding natural reach-and-grasp actions from human EEG. J. Neural Eng. 15(1), 016005 (2018). https:\/\/doi.org\/10.1088\/1741-2552\/aa8911","journal-title":"J. Neural Eng."},{"issue":"3","key":"26_CR20","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1016\/j.mayocp.2011.12.008","volume":"87","author":"JJ Shih","year":"2012","unstructured":"Shih, J.J., Krusienski, D.J., Wolpaw, J.R.: Brain-computer interfaces in medicine. Mayo Clin. Proc. 87(3), 268\u2013279 (2012)","journal-title":"Mayo Clin. Proc."},{"key":"26_CR21","doi-asserted-by":"publisher","first-page":"2754","DOI":"10.1109\/TNSRE.2022.3208710","volume":"30","author":"Y Tao","year":"2022","unstructured":"Tao, Y., et al.: Decoding multi-class EEG signals of hand movement using multivariate empirical mode decomposition and convolutional neural network. IEEE Trans. Neural Syst. Rehabil. Eng. 30, 2754\u20132763 (2022). https:\/\/doi.org\/10.1109\/TNSRE.2022.3208710","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"26_CR22","doi-asserted-by":"crossref","unstructured":"Tidare, J., Leon, M., Xiong, N., Astrand, E.: Discriminating EEG spectral power related to mental imagery of closing and opening of hand. In: 2019 9th International IEEE\/EMBS Conference on Neural Engineering (NER), pp. 307\u2013310. IEEE (2019)","DOI":"10.1109\/NER.2019.8717059"},{"issue":"3","key":"26_CR23","doi-asserted-by":"publisher","first-page":"031001","DOI":"10.1088\/1741-2560\/12\/3\/031001","volume":"12","author":"JA Urig\u00fcen","year":"2015","unstructured":"Urig\u00fcen, J.A., Garcia-Zapirain, B.: EEG artifact removal-state-of-the-art and guidelines. J. Neural Eng. 12(3), 031001 (2015)","journal-title":"J. Neural Eng."},{"key":"26_CR24","doi-asserted-by":"crossref","unstructured":"Vourvopoulos, A., Berm\u00fadez\u00a0i Badia, S.: Motor priming in virtual reality can augment motor-imagery training efficacy in restorative brain-computer interaction: a within-subject analysis. J. Neuroeng. Rehabil. 13(1), 1\u201314 (2016)","DOI":"10.1186\/s12984-016-0173-2"},{"key":"26_CR25","doi-asserted-by":"publisher","unstructured":"Wang, Y., Gao, S., Gao, X.: Common spatial pattern method for channel selection in motor imagery based brain-computer interface. In: 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, pp. 5392\u20135395 (2005). https:\/\/doi.org\/10.1109\/IEMBS.2005.1615701","DOI":"10.1109\/IEMBS.2005.1615701"},{"issue":"3","key":"26_CR26","doi-asserted-by":"publisher","first-page":"639","DOI":"10.1109\/TPAMI.2014.2330598","volume":"37","author":"W Wu","year":"2015","unstructured":"Wu, W., Chen, Z., Gao, X., Li, Y., Brown, E.N., Gao, S.: Probabilistic common spatial patterns for multichannel EEG analysis. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 639\u2013653 (2015). https:\/\/doi.org\/10.1109\/TPAMI.2014.2330598","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"26_CR27","doi-asserted-by":"publisher","first-page":"100100","DOI":"10.1016\/j.medntd.2021.100100","volume":"13","author":"B Yang","year":"2022","unstructured":"Yang, B., Ma, J., Qiu, W., Zhu, Y., Meng, X.: A new 2-class unilateral upper limb motor imagery tasks for stroke rehabilitation training. Med. Novel Technol. Devices 13, 100100 (2022)","journal-title":"Med. Novel Technol. Devices"},{"issue":"4","key":"26_CR28","doi-asserted-by":"publisher","first-page":"e0121896","DOI":"10.1371\/journal.pone.0121896","volume":"10","author":"X Yong","year":"2015","unstructured":"Yong, X., Menon, C.: EEG classification of different imaginary movements within the same limb. PLoS ONE 10(4), e0121896 (2015). https:\/\/doi.org\/10.1371\/journal.pone.0121896","journal-title":"PLoS ONE"},{"key":"26_CR29","doi-asserted-by":"publisher","unstructured":"Zhang, W., Song, A., Lai, J.: Motor imagery BCI-based online control soft glove rehabilitation system with vibrotactile stimulation. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds.) Neural Information Processing. ICONIP 2022. Communications in Computer and Information Science, vol. 1792, pp. 456\u2013466. Springer, Singapore (2023). https:\/\/doi.org\/10.1007\/978-981-99-1642-9_39","DOI":"10.1007\/978-981-99-1642-9_39"}],"container-title":["Communications in Computer and Information Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8138-0_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T17:34:16Z","timestamp":1710351256000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8138-0_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,26]]},"ISBN":["9789819981373","9789819981380"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8138-0_26","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,26]]},"assertion":[{"value":"26 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Changsha","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iconip2023.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1274","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"650","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"51% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4.14","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.46","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}