{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,16]],"date-time":"2025-04-16T10:49:54Z","timestamp":1744800594739,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":25,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789811982217"},{"type":"electronic","value":"9789811982224"}],"license":[{"start":{"date-parts":[[2022,11,29]],"date-time":"2022-11-29T00:00:00Z","timestamp":1669680000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,11,29]],"date-time":"2022-11-29T00:00:00Z","timestamp":1669680000000},"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":[[2023]]},"DOI":"10.1007\/978-981-19-8222-4_7","type":"book-chapter","created":{"date-parts":[[2022,11,29]],"date-time":"2022-11-29T02:18:15Z","timestamp":1669688295000},"page":"73-85","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Emotion Recognition from EEG Using All-Convolution Residual Neural Network"],"prefix":"10.1007","author":[{"given":"Hongyuan","family":"Xuan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9549-0177","authenticated-orcid":false,"given":"Jing","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5300-2283","authenticated-orcid":false,"given":"Penghui","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9532-8273","authenticated-orcid":false,"given":"Guanghua","family":"Gu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6468-1197","authenticated-orcid":false,"given":"Dong","family":"Cui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,11,29]]},"reference":[{"issue":"2","key":"7_CR1","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1016\/j.psychres.2016.05.037","volume":"24","author":"M Fieker","year":"2016","unstructured":"Fieker, M., Moritz, S., Jelinek, L.: Emotion recognition in depression: an investigation of performance and response confidence in adult female patients with depression. Psychiatry Res. 24(2), 226\u2013232 (2016)","journal-title":"Psychiatry Res."},{"issue":"2","key":"7_CR2","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1186\/s13229-016-0113-9","volume":"7","author":"S Fridenson-Hayo","year":"2016","unstructured":"Fridenson-Hayo, S.: Basic and complex emotion recognition in children with autism: cross-cultural findings. Mol. Autism 7(2), 52\u201359 (2016)","journal-title":"Mol. Autism"},{"issue":"2","key":"7_CR3","doi-asserted-by":"publisher","first-page":"1250002","DOI":"10.1142\/S0129065712500025","volume":"22","author":"UR Acharya","year":"2012","unstructured":"Acharya, U.R., Sree, S.V., Alvin, A.P., Yanti, R., Suri, J.S.: Application of non-linear and wavelet based features for the automated identification of epileptic EEG signals. Int. J. Neural Syst. 22(2), 1250002\u20131250002 (2012)","journal-title":"Int. J. Neural Syst."},{"issue":"1","key":"7_CR4","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1186\/1475-925X-12-44","volume":"12","author":"J Selvaraj","year":"2013","unstructured":"Selvaraj, J., Murugappan, M., Wan, K., Yaacob, S.: Classification of emotional states from electrocardiogram signals: a non-linear approach based on Hurst. Biomed. Eng. Online 12(1), 44\u201349 (2013)","journal-title":"Biomed. Eng. Online"},{"issue":"1","key":"7_CR5","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1109\/79.911197","volume":"18","author":"R Cowie","year":"2001","unstructured":"Cowie, R.: Emotion recognition in human-computer interaction. IEEE Signal Process. Mag. 18(1), 32\u201380 (2001)","journal-title":"IEEE Signal Process. Mag."},{"issue":"4","key":"7_CR6","doi-asserted-by":"publisher","first-page":"1165","DOI":"10.1093\/brain\/aws024","volume":"135","author":"MJ Novak","year":"2012","unstructured":"Novak, M.J., Warren, J.D., Henley, S.M., Draganski, B., Tabrizi, S.J.: Altered brain mechanisms of emotion processing in pre-manifest Huntington\u2019s disease. Brain 135(4), 1165\u20131179 (2012)","journal-title":"Brain"},{"issue":"9","key":"7_CR7","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1097\/PSY.0b013e318235ed55","volume":"73","author":"JA McCubbin","year":"2011","unstructured":"McCubbin, J.A.: Cardiovascular-emotional dampening: the relationship between blood pressure and recognition of emotion. Psychosom. Med. 73(9), 743\u2013750 (2011)","journal-title":"Psychosom. Med."},{"issue":"7","key":"7_CR8","doi-asserted-by":"publisher","first-page":"36","DOI":"10.3390\/s19071659","volume":"19","author":"F Al Machot","year":"2019","unstructured":"Al Machot, F., Elmachot, A., Ali, M., Al Machot, E., Kyamakya, K.: A deep-learning model for subject-independent human emotion recognition using electrodermal activity sensors. Sensors (Basel) 19(7), 36\u201347 (2019)","journal-title":"Sensors (Basel)"},{"issue":"2","key":"7_CR9","first-page":"317","volume":"420","author":"L Zhang","year":"2021","unstructured":"Zhang, L., Chen, D., Chen, P., Li, W., Li, X.: Dual-CNN based multi-modal sleep scoring with temporal correlation driven fine-tuning. Neurocomputing 420(2), 317\u2013328 (2021)","journal-title":"Neurocomputing"},{"issue":"4","key":"7_CR10","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1016\/j.neucom.2021.04.009","volume":"449","author":"H Dong","year":"2021","unstructured":"Dong, H., Chen, D., Zhang, L., Ke, H., Li, X.: Subject sensitive EEG discrimination with fast reconstructable CNN driven by reinforcement learning: a case study of ASD evaluation. Neurocomputing 449(4), 136\u2013145 (2021)","journal-title":"Neurocomputing"},{"issue":"4","key":"7_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3448302","volume":"54","author":"Y Tang","year":"2021","unstructured":"Tang, Y., Chen, D., Li, X.: Dimensionality reduction methods for brain imaging data analysis. ACM Comput. Surv. 54(4), 1\u201336 (2021)","journal-title":"ACM Comput. Surv."},{"issue":"3","key":"7_CR12","doi-asserted-by":"publisher","first-page":"589","DOI":"10.1109\/TITB.2010.2041553","volume":"14","author":"CA Frantzidis","year":"2010","unstructured":"Frantzidis, C.A., Bratsas, C., Papadelis, C.L., Konstantinidis, E., Pappas, C., Bamidis, P.D.: Toward emotion aware computing: an integrated approach using multichannel neurophysiological recordings and affective visual stimuli. IEEE Trans. Inf. Technol. Biomed. 14(3), 589\u2013597 (2010)","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"issue":"3","key":"7_CR13","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1109\/TAFFC.2017.2712143","volume":"10","author":"W-L Zheng","year":"2019","unstructured":"Zheng, W.-L., Zhu, J.-Y., Lu, B.-L.: Identifying stable patterns over time for emotion recognition from EEG. IEEE Trans. Affect. Comput. 10(3), 417\u2013429 (2019)","journal-title":"IEEE Trans. Affect. Comput."},{"issue":"8","key":"7_CR14","doi-asserted-by":"publisher","first-page":"1985","DOI":"10.1007\/s00521-015-2149-8","volume":"28","author":"Z Mohammadi","year":"2016","unstructured":"Mohammadi, Z., Frounchi, J., Amiri, M.: Wavelet-based emotion recognition system using EEG signal. Neural Comput. Appl. 28(8), 1985\u20131990 (2016). https:\/\/doi.org\/10.1007\/s00521-015-2149-8","journal-title":"Neural Comput. Appl."},{"issue":"10","key":"7_CR15","first-page":"29","volume":"8","author":"S Alhagry","year":"2017","unstructured":"Alhagry, S., Fahmy, A.A., El-Khoribi, R.A.: Emotion recognition based on EEG using LSTM recurrent neural network. Int. J. Adv. Comput. Sci. Appl. 8(10), 29\u201337 (2017)","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"7_CR16","doi-asserted-by":"crossref","unstructured":"Wang, Y., Huang, Z., McCane, B., Neo, P.: EmotioNet: a 3-D convolutional neural network for EEG-based emotion recognition. In: 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20137, Rio de Janeiro, Brazil (2018)","DOI":"10.1109\/IJCNN.2018.8489715"},{"issue":"5","key":"7_CR17","doi-asserted-by":"publisher","first-page":"1383","DOI":"10.3390\/s18051383","volume":"18","author":"YH Kwon","year":"2018","unstructured":"Kwon, Y.H., Shin, S.B., Kim, S.D.: Electroencephalography based fusion two-dimensional (2D)-convolution neural networks (CNN) model for emotion recognition system. Sensors (Basel) 18(5), 1383\u20131395 (2018)","journal-title":"Sensors (Basel)"},{"key":"7_CR18","doi-asserted-by":"crossref","unstructured":"Duan, R.N., Zhu, J.Y., Lu, B.L.: Differential entropy feature for EEG-based emotion classification. In: 6th International IEEE\/EMBS Conference on Neural Engineering, pp. 81\u201384, San Diego, CA, USA (2013)","DOI":"10.1109\/NER.2013.6695876"},{"key":"7_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1007\/978-3-030-04239-4_39","volume-title":"Neural Information Processing","author":"Y Yang","year":"2018","unstructured":"Yang, Y., Wu, Q., Fu, Y., Chen, X.: Continuous convolutional neural network with 3D input for EEG-based emotion recognition. In: Cheng, L., Leung, A.C.S., Ozawa, S. (eds.) ICONIP 2018. LNCS, vol. 11307, pp. 433\u2013443. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-04239-4_39"},{"key":"7_CR20","unstructured":"Springenberg, J.T., Dosovitskiy, A., Brox, T., Riedmiller, M.: Striving for Simplicity: the All Convolutional Net (2014)"},{"key":"7_CR21","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"1","key":"7_CR22","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/T-AFFC.2011.15","volume":"3","author":"S Koelstra","year":"2012","unstructured":"Koelstra, S.: DEAP: a database for emotion analysis; using physiological signals. IEEE Trans. Affect. Comput. 3(1), 18\u201331 (2012)","journal-title":"IEEE Trans. Affect. Comput."},{"issue":"1","key":"7_CR23","doi-asserted-by":"publisher","first-page":"37","DOI":"10.3389\/fnbot.2019.00037","volume":"13","author":"X Xing","year":"2019","unstructured":"Xing, X., Li, Z., Xu, T., Shu, L., Hu, B., Xu, X.: SAE+LSTM: a new framework for emotion recognition from multi-channel EEG. Front. Neurorobot. 13(1), 37\u201345 (2019)","journal-title":"Front. Neurorobot."},{"key":"7_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1007\/978-3-319-71589-6_33","volume-title":"Image and Graphics","author":"W Lin","year":"2017","unstructured":"Lin, W., Li, C., Sun, S.: Deep convolutional neural network for emotion recognition using EEG and peripheral physiological signal. In: Zhao, Y., Kong, X., Taubman, D. (eds.) ICIG 2017. LNCS, vol. 10667, pp. 385\u2013394. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-71589-6_33"},{"issue":"8","key":"7_CR25","first-page":"329","volume":"9","author":"ES Salama","year":"2018","unstructured":"Salama, E.S., El-Khoribi, R.A., Shoman, M.E., Shalaby, M.A.W.: EEG-based emotion recognition using 3D convolutional neural networks. Int. J. Adv. Comput. Sci. Appl. 9(8), 329\u2013336 (2018)","journal-title":"Int. J. Adv. Comput. Sci. Appl."}],"container-title":["Communications in Computer and Information Science","Human Brain and Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-19-8222-4_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,29]],"date-time":"2022-11-29T02:20:05Z","timestamp":1669688405000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-19-8222-4_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,29]]},"ISBN":["9789811982217","9789811982224"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-981-19-8222-4_7","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2022,11,29]]},"assertion":[{"value":"29 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"HBAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Human Brain and Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vienna","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Austria","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":"23 July 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 July 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ijcaihbai2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/hbai2022.github.io\/","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":"Easy chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"21","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":"19","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":"90% - 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":"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)"}}]}}