{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T23:39:24Z","timestamp":1768261164313,"version":"3.49.0"},"publisher-location":"Cham","reference-count":15,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031138218","type":"print"},{"value":"9783031138225","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\/978-3-031-13822-5_64","type":"book-chapter","created":{"date-parts":[[2022,8,3]],"date-time":"2022-08-03T18:18:24Z","timestamp":1659550704000},"page":"711-721","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Error Related Potential Classification Using a 2-D Convolutional Neural Network"],"prefix":"10.1007","author":[{"given":"Yuxiang","family":"Gao","sequence":"first","affiliation":[]},{"given":"Tangfei","family":"Tao","sequence":"additional","affiliation":[]},{"given":"Yaguang","family":"Jia","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,8,4]]},"reference":[{"key":"64_CR1","doi-asserted-by":"publisher","unstructured":"Baniqued, P.D.E., Stanyer, E.C., Awais, M., Alazmani, A., Holt, R.J.: Brain-computer interface robotics for hand rehabilitation after stroke: a systematic review. J. NeuroEng. Rehabil. 18 (2021). https:\/\/doi.org\/10.1101\/2019.12.11.19014571","DOI":"10.1101\/2019.12.11.19014571"},{"key":"64_CR2","doi-asserted-by":"publisher","unstructured":"L\u00f3pez-Larraz, E., et al.: Brain-machine interfaces for rehabilitation in stroke: a review. Neurorehabilitation 43, 77\u201397 (2018). https:\/\/doi.org\/10.3233\/NRE-17239","DOI":"10.3233\/NRE-17239"},{"key":"64_CR3","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1186\/1753-4631-3-2","volume":"3","author":"W Klonowski","year":"2009","unstructured":"Klonowski, W.: Everything you wanted to ask about EEG but were afraid to get the right answer. Nonlinear Biomed. Phys. 3, 2 (2009). https:\/\/doi.org\/10.1186\/1753-4631-3-2","journal-title":"Nonlinear Biomed. Phys."},{"key":"64_CR4","doi-asserted-by":"publisher","unstructured":"Pascual-Marqui, R.D.: Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. Methods Find. Exp. Clin. Pharmacol. 24(Suppl. D), 5\u201312 (2002). https:\/\/doi.org\/10.1002\/med.10000","DOI":"10.1002\/med.10000"},{"key":"64_CR5","doi-asserted-by":"publisher","unstructured":"Chavarriaga, R., Sobolewski, A., Mill\u00e1n, J.d.R.: Errare machinale est: the use of error-related potentials in brain-machine interfaces. Front. Neurosci. 8, 208 (2014). https:\/\/doi.org\/10.3389\/fnins.2014.00208","DOI":"10.3389\/fnins.2014.00208"},{"key":"64_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.103515","volume":"74","author":"PK Parashiva","year":"2022","unstructured":"Parashiva, P.K., Vinod, A.P.: Improving direction decoding accuracy during online motor imagery based brain-computer interface using error-related potentials. Biomed. Signal Process. Control 74, 103515 (2022). https:\/\/doi.org\/10.1016\/j.bspc.2022.103515","journal-title":"Biomed. Signal Process. Control"},{"key":"64_CR7","doi-asserted-by":"publisher","unstructured":"Chavarriaga, R., Millan, J.d.R.: Learning from EEG error-related potentials in noninvasive brain-computer interfaces. IEEE Trans. Neural Syst. Rehabil. Eng. 18, 381\u20133888 (2010). https:\/\/doi.org\/10.1109\/TNSRE.2010.2053387","DOI":"10.1109\/TNSRE.2010.2053387"},{"key":"64_CR8","doi-asserted-by":"publisher","unstructured":"Kumar, A., Pirogova, E., Fang, J.Q.: Classification of error-related potentials using linear discriminant analysis. In: 2018 IEEE-EMBS Conference on Biomedical Engineering and Sciences, pp. 18\u201321 (2018). https:\/\/doi.org\/10.1109\/IECBES.2018.8626709","DOI":"10.1109\/IECBES.2018.8626709"},{"key":"64_CR9","doi-asserted-by":"publisher","unstructured":"Torres, J.M.M., Clarkson, T., Stepanov, E.A., Luhmann, C.C., Lerner, M.D., Riccardi, G.: Enhanced error decoding from error-related potentials using convolutional neural networks. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 360\u2013363 (2018). https:\/\/doi.org\/10.1109\/EMBC.2018.8512183","DOI":"10.1109\/EMBC.2018.8512183"},{"key":"64_CR10","doi-asserted-by":"publisher","unstructured":"Swamy Bellary, S.A., Conrad, J.M.: Classification of error related potentials using convolutional neural networks. In: 2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence), pp. 245\u2013249 (2019). https:\/\/doi.org\/10.1109\/CONFLUENCE.2019.8776901","DOI":"10.1109\/CONFLUENCE.2019.8776901"},{"key":"64_CR11","doi-asserted-by":"publisher","unstructured":"Parashiva, P.K., Vinod, A.P.: Improving classification accuracy of detecting error-related potentials using two-stage trained neural network classifier. In: 2020 11th International Conference on Awareness Science and Technology (iCAST), pp. 1\u20135 (2020). https:\/\/doi.org\/10.1109\/iCAST51195.2020.9319482","DOI":"10.1109\/iCAST51195.2020.9319482"},{"key":"64_CR12","doi-asserted-by":"publisher","unstructured":"Ullah, A., et al.: A hybrid deep CNN model for abnormal arrhythmia detection based on cardiac ECG signal. Sensors 21 (2021). https:\/\/doi.org\/10.3390\/s21030951","DOI":"10.3390\/s21030951"},{"key":"64_CR13","doi-asserted-by":"publisher","first-page":"201","DOI":"10.48550\/arXiv.1804.06812","volume":"11","author":"R Rohmantri","year":"2020","unstructured":"Rohmantri, R., Surantha, N.: Arrhythmia classification using 2D convolutional neural network. Int. J. Adv. Comput. Sci. Appl. 11, 201\u2013208 (2020). https:\/\/doi.org\/10.48550\/arXiv.1804.06812","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"64_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-01234-2_1","volume-title":"Computer Vision \u2013 ECCV 2018","author":"S Woo","year":"2018","unstructured":"Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 3\u201319. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_1"},{"key":"64_CR15","doi-asserted-by":"publisher","unstructured":"Szegedy, C., et al.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818\u20132826 (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.308","DOI":"10.1109\/CVPR.2016.308"}],"container-title":["Lecture Notes in Computer Science","Intelligent Robotics and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-13822-5_64","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,15]],"date-time":"2022-08-15T23:13:29Z","timestamp":1660605209000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-13822-5_64"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031138218","9783031138225"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-13822-5_64","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":"4 August 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIRA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Robotics and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Harbin","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":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 August 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 August 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icira2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icira2022.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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"442","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":"284","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":"64% - 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":"5","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)"}}]}}