{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,17]],"date-time":"2026-05-17T03:53:48Z","timestamp":1778990028954,"version":"3.51.4"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031024436","type":"print"},{"value":"9783031024443","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-02444-3_20","type":"book-chapter","created":{"date-parts":[[2022,5,9]],"date-time":"2022-05-09T12:02:50Z","timestamp":1652097770000},"page":"268-281","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Subject-Independent Motor Imagery EEG Classification Based on\u00a0Graph Convolutional Network"],"prefix":"10.1007","author":[{"given":"Juho","family":"Lee","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jin Woo","family":"Choi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sungho","family":"Jo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,5,10]]},"reference":[{"issue":"8","key":"20_CR1","doi-asserted-by":"publisher","first-page":"3996","DOI":"10.1109\/JSEN.2019.2962874","volume":"20","author":"P Sawangjai","year":"2019","unstructured":"Sawangjai, P., Hompoonsup, S., Leelaarporn, P., Kongwudhikunakorn, S., Wilaiprasitporn, T.: Consumer grade EEG measuring sensors as research tools: a review. IEEE Sens. J. 20(8), 3996\u20134024 (2019)","journal-title":"IEEE Sens. J."},{"key":"20_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.104079","volume":"127","author":"JW Choi","year":"2020","unstructured":"Choi, J.W., 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":"20_CR3","doi-asserted-by":"crossref","unstructured":"Kim, B.H., Jo, S., Choi, S.: ALIS: learning affective causality behind daily activities from a wearable life-log system. IEEE Trans. Cybern. (2021)","DOI":"10.1109\/TCYB.2021.3106638"},{"issue":"1","key":"20_CR4","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/abd10e","volume":"18","author":"N Kaongoen","year":"2021","unstructured":"Kaongoen, N., Choi, J., Jo, S.: Speech-imagery-based brain-computer interface system using ear-EEG. J. Neural Eng. 18(1), 016023 (2021)","journal-title":"J. Neural Eng."},{"key":"20_CR5","doi-asserted-by":"crossref","unstructured":"Gao, Z., et al.: EEG-based spatio-temporal convolutional neural network for driver fatigue evaluation. IEEE Trans. Neural Netw. Learn. Syst. 30(9), 2755\u20132763 (2019)","DOI":"10.1109\/TNNLS.2018.2886414"},{"key":"20_CR6","doi-asserted-by":"crossref","unstructured":"Vidyaratne, L.S., Iftekharuddin, K.M.: Real-time epileptic seizure detection using EEG. IEEE Trans. Neural Syst. Rehabil. Eng. 25(11), 2146\u20132156 (2017)","DOI":"10.1109\/TNSRE.2017.2697920"},{"key":"20_CR7","doi-asserted-by":"crossref","unstructured":"Chakladar, D.D., Dey, S., Roy, P.P., Iwamura, M.: EEG-based cognitive state assessment using deep ensemble model and filter bank common spatial pattern. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 4107\u20134114. IEEE (2021)","DOI":"10.1109\/ICPR48806.2021.9412869"},{"key":"20_CR8","doi-asserted-by":"crossref","unstructured":"Chakladar, D.D., Dey, S., Roy, P.P., Dogra, D.P.: EEG-based mental workload estimation using deep BLSTM-LSTM network and evolutionary algorithm. Biomed. Signal Process. Control 60, 101989 (2020)","DOI":"10.1016\/j.bspc.2020.101989"},{"key":"20_CR9","doi-asserted-by":"crossref","unstructured":"Autthasan, P., et al.: A single-channel consumer-grade EEG device for brain-computer interface: enhancing detection of SSVEP and its amplitude modulation. IEEE Sens. J. 20(6), 3366\u20133378 (2019)","DOI":"10.1109\/JSEN.2019.2958210"},{"key":"20_CR10","doi-asserted-by":"crossref","unstructured":"Zou, Y., Nathan, V., Jafari, R.: Automatic identification of artifact-related independent components for artifact removal in EEG recordings. IEEE J. Biomed. Health Inform. 20(1), 73\u201381 (2014)","DOI":"10.1109\/JBHI.2014.2370646"},{"issue":"3","key":"20_CR11","doi-asserted-by":"publisher","first-page":"687","DOI":"10.1109\/TNSRE.2020.2966826","volume":"28","author":"J-H Jeong","year":"2020","unstructured":"Jeong, J.-H., Kwak, N.-S., Guan, C., Lee, S.-W.: Decoding movement-related cortical potentials based on subject-dependent and section-wise spectral filtering. IEEE Trans. Neural Syst. Rehabil. Eng. 28(3), 687\u2013698 (2020)","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"issue":"7","key":"20_CR12","doi-asserted-by":"publisher","first-page":"1614","DOI":"10.1109\/TNSRE.2020.2998123","volume":"28","author":"JW Choi","year":"2020","unstructured":"Choi, J.W., Kim, B.H., Huh, S., Jo, S.: Observing actions through immersive virtual reality enhances motor imagery training. IEEE Trans. Neural Syst. Rehabil. Eng. 28(7), 1614\u20131622 (2020)","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"20_CR13","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25, pp. 1097\u20131105 (2012)"},{"key":"20_CR14","unstructured":"Amodei, D., et al.: Deep speech 2: end-to-end speech recognition in English and Mandarin. In: International Conference on Machine Learning, pp. 173\u2013182. PMLR (2016)"},{"key":"20_CR15","doi-asserted-by":"crossref","unstructured":"Ang, K.K., Guan, C.: EEG-based strategies to detect motor imagery for control and rehabilitation. IEEE Trans. Neural Syst. Rehabil. Eng. 25(4), 392\u2013401 (2016)","DOI":"10.1109\/TNSRE.2016.2646763"},{"issue":"7","key":"20_CR16","doi-asserted-by":"publisher","first-page":"1123","DOI":"10.1109\/5.939829","volume":"89","author":"G Pfurtscheller","year":"2001","unstructured":"Pfurtscheller, G., Neuper, C.: Motor imagery and direct brain-computer communication. Proc. IEEE 89(7), 1123\u20131134 (2001)","journal-title":"Proc. IEEE"},{"key":"20_CR17","doi-asserted-by":"crossref","unstructured":"Schirrmeister, R.T., et al.: Deep learning with convolutional neural networks for EEG decoding and visualization. Hum. Brain Mapp. 38(11), 5391\u20135420 (2017)","DOI":"10.1002\/hbm.23730"},{"key":"20_CR18","doi-asserted-by":"crossref","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-computer interfaces. J. Neural Eng. 15(5), 056013 (2018)","DOI":"10.1088\/1741-2552\/aace8c"},{"issue":"2","key":"20_CR19","first-page":"230","volume":"11","author":"BH Kim","year":"2018","unstructured":"Kim, B.H., Jo, S.: Deep physiological affect network for the recognition of human emotions. IEEE Trans. Affect. Comput. 11(2), 230\u2013243 (2018)","journal-title":"IEEE Trans. Affect. Comput."},{"issue":"6","key":"20_CR20","doi-asserted-by":"publisher","first-page":"871","DOI":"10.1109\/JPROC.2015.2404941","volume":"103","author":"F Lotte","year":"2015","unstructured":"Lotte, F.: Signal processing approaches to minimize or suppress calibration time in oscillatory activity-based brain-computer interfaces. Proc. IEEE 103(6), 871\u2013890 (2015)","journal-title":"Proc. IEEE"},{"key":"20_CR21","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":"20_CR22","unstructured":"Blankertz, B., Kawanabe, M., Tomioka, R., Hohlefeld, F.U., Nikulin, V.V., M\u00fcller, K.-R.: Invariant common spatial patterns: alleviating nonstationarities in brain-computer interfacing. In: NIPS, pp. 113\u2013120 (2007)"},{"key":"20_CR23","doi-asserted-by":"crossref","unstructured":"Wang, H., Zheng, W.: Local temporal common spatial patterns for robust single-trial EEG classification. IEEE Trans. Neural Syst. Rehabil. Eng. 16(2), 131\u2013139 (2008)","DOI":"10.1109\/TNSRE.2007.914468"},{"key":"20_CR24","doi-asserted-by":"crossref","unstructured":"Ramoser, H., Muller-Gerking, J., Pfurtscheller, G.: Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans. Rehabil. Eng. 8(4), 441\u2013446 (2000)","DOI":"10.1109\/86.895946"},{"issue":"2","key":"20_CR25","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1016\/j.neuroimage.2007.01.051","volume":"37","author":"B Blankertz","year":"2007","unstructured":"Blankertz, B., Dornhege, G., Krauledat, M., M\u00fcller, K.-R., Curio, G.: The non-invasive berlin brain-computer interface: fast acquisition of effective performance in untrained subjects. Neuroimage 37(2), 539\u2013550 (2007)","journal-title":"Neuroimage"},{"key":"20_CR26","doi-asserted-by":"crossref","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)","DOI":"10.3389\/fnins.2012.00039"},{"key":"20_CR27","unstructured":"Bishop, C.M.: Pattern recognition. In: Machine learning, vol. 128, no. 9 (2006)"},{"key":"20_CR28","doi-asserted-by":"crossref","unstructured":"Fraiwan, L., Lweesy, K., Khasawneh, N., Wenz, H., Dickhaus, H.: Automated sleep stage identification system based on time-frequency analysis of a single EEG channel and random forest classifier. Comput. Methods Programs Biomed. 108(1), 10\u201319 (2012)","DOI":"10.1016\/j.cmpb.2011.11.005"},{"key":"20_CR29","doi-asserted-by":"crossref","unstructured":"Tang, X., Zhang, X.: Conditional adversarial domain adaptation neural network for motor imagery EEG decoding. Entropy 22(1), 96 (2020)","DOI":"10.3390\/e22010096"},{"key":"20_CR30","doi-asserted-by":"crossref","unstructured":"An, S., Kim, S., Chikontwe, P., Park, S.H.: Few-shot relation learning with attention for EEG-based motor imagery classification. In: 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 10933\u201310938. IEEE (2020)","DOI":"10.1109\/IROS45743.2020.9340933"},{"key":"20_CR31","doi-asserted-by":"crossref","unstructured":"Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P.H., Hospedales, T.M.: Learning to compare: relation network for few-shot learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1199\u20131208 (2018)","DOI":"10.1109\/CVPR.2018.00131"},{"key":"20_CR32","doi-asserted-by":"publisher","unstructured":"Wang, H., Xu, M., Ni, B., Zhang, W.: Learning to combine: knowledge aggregation for multi-source domain adaptation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12353, pp. 727\u2013744. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58598-3_43","DOI":"10.1007\/978-3-030-58598-3_43"},{"key":"20_CR33","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)"},{"key":"20_CR34","unstructured":"Brunner, C., Leeb, R., M\u00fcller-Putz, G., Schl\u00f6gl, A., Pfurtscheller, G.: BCI competition 2008-Graz data set A, vol. 16, pp. 1\u20136. Institute for Knowledge Discovery (Laboratory of Brain-Computer Interfaces), Graz University of Technology (2008)"},{"key":"20_CR35","doi-asserted-by":"crossref","unstructured":"Caruana, R., Lawrence, S., Giles, L.: Overfitting in neural nets: backpropagation, conjugate gradient, and early stopping. In: Advances in Neural Information Processing Systems, pp. 402\u2013408 (2001)","DOI":"10.1109\/IJCNN.2000.857823"},{"key":"20_CR36","unstructured":"Donahue, J., et al.: DeCAF: a deep convolutional activation feature for generic visual recognition. In: International Conference on Machine Learning, pp. 647\u2013655. PMLR (2014)"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-02444-3_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,9]],"date-time":"2022-05-09T12:07:21Z","timestamp":1652098041000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-02444-3_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031024436","9783031024443"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-02444-3_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"10 May 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Jeju Island","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","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":"9 November 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 November 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"acpr2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.acpr2021.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":"154","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":"85","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":"4","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}