{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T03:38:23Z","timestamp":1743046703438,"version":"3.40.3"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030922375"},{"type":"electronic","value":"9783030922382"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-92238-2_26","type":"book-chapter","created":{"date-parts":[[2021,12,4]],"date-time":"2021-12-04T22:02:35Z","timestamp":1638655355000},"page":"311-322","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Multi-task Learning Scheme for\u00a0Motor Imagery Signal Classification"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2825-7030","authenticated-orcid":false,"given":"Rahul","family":"Kumar","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5458-9381","authenticated-orcid":false,"given":"Sriparna","family":"Saha","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,12,5]]},"reference":[{"key":"26_CR1","doi-asserted-by":"crossref","unstructured":"Wolpaw, J., Wolpaw, E.W.: Brain-Computer Interfaces: Principles and Practice. Oxford University Press, USA (2012)","DOI":"10.1093\/acprof:oso\/9780195388855.001.0001"},{"issue":"2","key":"26_CR2","first-page":"1","volume":"2","author":"M Teplan","year":"2002","unstructured":"Teplan, M., et al.: Fundamentals of EEG measurement. Meas. Sci. Rev. 2(2), 1\u201311 (2002)","journal-title":"Meas. Sci. Rev."},{"key":"26_CR3","unstructured":"Ang, K.K., Chin, Z.Y., Zhang, H., Guan, C.: Filter bank common spatial pattern (FBCSP) in brain-computer interface. In: 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), pp. 2390\u20132397. IEEE (2008)"},{"key":"26_CR4","doi-asserted-by":"publisher","first-page":"39","DOI":"10.3389\/fnins.2012.00039","volume":"6","author":"KK Ang","year":"2012","unstructured":"Ang, K.K., Chin, Z.Y., Wang, C., Guan, C., Zhang, H.: Filter bank common spatial pattern algorithm on BCI competition iv datasets 2a and 2b. Front. Neurosci. 6, 39 (2012)","journal-title":"Front. Neurosci."},{"key":"26_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jneumeth.2014.02.014","volume":"228","author":"AX Stewart","year":"2014","unstructured":"Stewart, A.X., Nuthmann, A., Sanguinetti, G.: Single-trial classification of EEG in a visual object task using ICA and machine learning. J. Neurosci. Methods 228, 1\u201314 (2014)","journal-title":"J. Neurosci. Methods"},{"key":"26_CR6","doi-asserted-by":"crossref","unstructured":"Lotte, F., Congedo, M., L\u00e9cuyer, A., Lamarche, F., Arnaldi, B.: A review of classification algorithms for EEG-based brain-computer interfaces. J. Neural Eng. 4(2), R1 (2007)","DOI":"10.1088\/1741-2560\/4\/2\/R01"},{"key":"26_CR7","doi-asserted-by":"crossref","unstructured":"Vaid, S., Singh, P., Kaur, C.: EEG signal analysis for BCI interface: a review. In: 2015 Fifth International Conference on Advanced Computing & Communication Technologies, pp. 143\u2013147. IEEE (2015)","DOI":"10.1109\/ACCT.2015.72"},{"issue":"2","key":"26_CR8","doi-asserted-by":"publisher","first-page":"1211","DOI":"10.3390\/s120201211","volume":"12","author":"LF Nicolas-Alonso","year":"2012","unstructured":"Nicolas-Alonso, L.F., Gomez-Gil, J.: Brain computer interfaces, a review. Sensors 12(2), 1211\u20131279 (2012)","journal-title":"Sensors"},{"issue":"1","key":"26_CR9","doi-asserted-by":"publisher","first-page":"016003","DOI":"10.1088\/1741-2560\/14\/1\/016003","volume":"14","author":"YR Tabar","year":"2016","unstructured":"Tabar, Y.R., Halici, U.: A novel deep learning approach for classification of EEG motor imagery signals. J. Neural Eng. 14(1), 016003 (2016)","journal-title":"J. Neural Eng."},{"issue":"3","key":"26_CR10","doi-asserted-by":"publisher","first-page":"551","DOI":"10.3390\/s19030551","volume":"19","author":"M Dai","year":"2019","unstructured":"Dai, M., Zheng, D., Na, R., Wang, S., Zhang, S.: EEG classification of motor imagery using a novel deep learning framework. Sensors 19(3), 551 (2019)","journal-title":"Sensors"},{"key":"26_CR11","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.ijleo.2016.10.117","volume":"130","author":"Z Tang","year":"2017","unstructured":"Tang, Z., Li, C., Sun, S.: Single-trial EEG classification of motor imagery using deep convolutional neural networks. Optik 130, 11\u201318 (2017)","journal-title":"Optik"},{"issue":"11","key":"26_CR12","doi-asserted-by":"publisher","first-page":"2086","DOI":"10.1109\/TNSRE.2018.2876129","volume":"26","author":"P Wang","year":"2018","unstructured":"Wang, P., Jiang, A., Liu, X., Shang, J., Zhang, L.: LSTM-based EEG classification in motor imagery tasks. IEEE Trans. Neural Syst. Rehabil. Eng. 26(11), 2086\u20132095 (2018)","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"issue":"6","key":"26_CR13","doi-asserted-by":"publisher","first-page":"566","DOI":"10.1109\/TNSRE.2016.2601240","volume":"25","author":"N Lu","year":"2016","unstructured":"Lu, N., Li, T., Ren, X., Miao, H.: A deep learning scheme for motor imagery classification based on restricted boltzmann machines. IEEE Trans. Neural Syst. Rehabil. Eng. 25(6), 566\u2013576 (2016)","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"26_CR14","unstructured":"Akhtar, S., Ghosal, D., Ekbal, A., Bhattacharyya, P., Kurohashi, S.: All-in-one: emotion, sentiment and intensity prediction using a multi-task ensemble framework. IEEE Trans. Affect. Comput. (2019)"},{"issue":"3","key":"26_CR15","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1109\/MCI.2020.2998234","volume":"15","author":"SA Qureshi","year":"2020","unstructured":"Qureshi, S.A., Dias, G., Hasanuzzaman, M., Saha, S.: Improving depression level estimation by concurrently learning emotion intensity. IEEE Comput. Intell. Mag. 15(3), 47\u201359 (2020)","journal-title":"IEEE Comput. Intell. Mag."},{"key":"26_CR16","doi-asserted-by":"crossref","unstructured":"Ko, W., Yoon, J., Kang, E., Jun, E., Choi, J.S., Suk, H.I.: Deep recurrent spatio-temporal neural network for motor imagery based BCI. In: 2018 6th International Conference on Brain-Computer Interface (BCI), pp. 1\u20133. IEEE (2018)","DOI":"10.1109\/IWW-BCI.2018.8311535"},{"key":"26_CR17","unstructured":"Leeb, R., Brunner, C., M\u00fcller-Putz, G., Schl\u00f6gl, A., Pfurtscheller, G.: BCI competition 2008-Graz data set b, pp. 1\u20136. Graz University of Technology, Austria (2008)"}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-92238-2_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T18:53:36Z","timestamp":1710356016000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-92238-2_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030922375","9783030922382"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-92238-2_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"5 December 2021","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":"Sanur, Bali","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Indonesia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 December 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 December 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iconip2021.apnns.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":"1093","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":"226","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":"177","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":"21% - 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":"2.57","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":"6","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)"}},{"value":"Due to the COVID-19 pandemic the conference was held online.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}