{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:45:40Z","timestamp":1742913940894,"version":"3.40.3"},"publisher-location":"Cham","reference-count":8,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030339036"},{"type":"electronic","value":"9783030339043"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"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":[[2019]]},"DOI":"10.1007\/978-3-030-33904-3_58","type":"book-chapter","created":{"date-parts":[[2019,10,25]],"date-time":"2019-10-25T22:40:05Z","timestamp":1572043205000},"page":"620-628","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Supervised Relevance Analysis for Multiple Stein Kernels for Spatio-Spectral Component Selection in BCI Discrimination Tasks"],"prefix":"10.1007","author":[{"given":"Camilo","family":"L\u00f3pez-Montes","sequence":"first","affiliation":[]},{"given":"David","family":"C\u00e1rdenas-Pe\u00f1a","sequence":"additional","affiliation":[]},{"given":"G.","family":"Castellanos-Dominguez","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,10,22]]},"reference":[{"issue":"7","key":"58_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1093\/gigascience\/gix034","volume":"6","author":"H Cho","year":"2017","unstructured":"Cho, H., Ahn, M., Ahn, S., Kwon, M., Jun, S.C.: EEG datasets for motor imagery brain-computer interface. GigaScience 6(7), 1\u20138 (2017)","journal-title":"GigaScience"},{"issue":"3","key":"58_CR2","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1007\/s11571-010-9117-x","volume":"4","author":"G Huang","year":"2010","unstructured":"Huang, G., Liu, G., Zhang, D., Zhu, X.: Model based generalization analysis of common spatial pattern in brain computer interfaces. Cogn. Neurodyn. 4(3), 217\u2013223 (2010)","journal-title":"Cogn. Neurodyn."},{"key":"58_CR3","doi-asserted-by":"publisher","first-page":"1871","DOI":"10.1016\/j.neucom.2017.10.013","volume":"275","author":"CH Nguyen","year":"2018","unstructured":"Nguyen, C.H., Artemiadis, P.: EEG feature descriptors and discriminant analysis under Riemannian manifold perspective. Neurocomputing 275, 1871\u20131883 (2018)","journal-title":"Neurocomputing"},{"issue":"5","key":"58_CR4","doi-asserted-by":"publisher","first-page":"537","DOI":"10.1515\/revneuro-2013-0032","volume":"24","author":"A Ortiz-Rosario","year":"2013","unstructured":"Ortiz-Rosario, A., Adeli, H.: Brain-computer interface technologies: from signal to action. Rev. Neurosci. 24(5), 537\u2013552 (2013)","journal-title":"Rev. Neurosci."},{"issue":"10","key":"58_CR5","doi-asserted-by":"publisher","first-page":"1753","DOI":"10.1109\/TNSRE.2016.2627016","volume":"25","author":"F Yger","year":"2017","unstructured":"Yger, F., Berar, M., Lotte, F.: Riemannian approaches in brain-computer interfaces: a review. IEEE Trans. Neural Syst. Rehabil. Eng. 25(10), 1753\u20131762 (2017)","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"58_CR6","doi-asserted-by":"crossref","unstructured":"Yger, F., Lotte, F., Sugiyama, M.: Averaging covariance matrices for EEG signal classification based on the CSP: an empirical study. In: 2015 23rd European Signal Processing Conference (EUSIPCO), pp. 2721\u20132725 (2015)","DOI":"10.1109\/EUSIPCO.2015.7362879"},{"issue":"02","key":"58_CR7","doi-asserted-by":"publisher","first-page":"1650032","DOI":"10.1142\/S0129065716500325","volume":"27","author":"Y Zhang","year":"2017","unstructured":"Zhang, Y., Wang, Y., Jin, J., Wang, X.: Sparse Bayesian learning for obtaining sparsity of EEG frequency bands based feature vectors in motor imagery classification. Int. J. Neural Syst. 27(02), 1650032 (2017)","journal-title":"Int. J. Neural Syst."},{"key":"58_CR8","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.jneumeth.2015.08.004","volume":"255","author":"Y Zhang","year":"2015","unstructured":"Zhang, Y., Zhou, G., Jin, J., Wang, X., Cichocki, A.: Optimizing spatial patterns with sparse filter bands for motor-imagery based brain-computer interface. J. Neurosci. Methods 255, 85\u201391 (2015)","journal-title":"J. Neurosci. Methods"}],"container-title":["Lecture Notes in Computer Science","Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-33904-3_58","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T14:12:18Z","timestamp":1710252738000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-33904-3_58"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030339036","9783030339043"],"references-count":8,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-33904-3_58","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"22 October 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CIARP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Iberoamerican Congress on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Havana","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cuba","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 October 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31 October 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ciarp2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/ciarp.uci.cu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"OCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"128","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":"70","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":"55% - 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":"3","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":"3","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":"This content has been made available to all.","name":"free","label":"Free to read"}]}}