{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T05:11:34Z","timestamp":1742965894187,"version":"3.40.3"},"publisher-location":"Cham","reference-count":38,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030911805"},{"type":"electronic","value":"9783030911812"}],"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\/978-3-030-91181-2_9","type":"book-chapter","created":{"date-parts":[[2022,2,10]],"date-time":"2022-02-10T04:57:39Z","timestamp":1644469059000},"page":"141-160","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Enhancing EEG-Based Emotion Recognition with Fast Online Instance Transfer"],"prefix":"10.1007","author":[{"given":"Hao","family":"Chen","sequence":"first","affiliation":[]},{"given":"Huiguang","family":"He","sequence":"additional","affiliation":[]},{"given":"Ting","family":"Cai","sequence":"additional","affiliation":[]},{"given":"Jinpeng","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,2,24]]},"reference":[{"issue":"5596","key":"9_CR1","doi-asserted-by":"publisher","first-page":"1191","DOI":"10.1126\/science.1076358","volume":"298","author":"RJ Dolan","year":"2002","unstructured":"Dolan, R.J.: Emotion, cognition, and behavior. Science 298(5596), 1191\u20131194 (2002)","journal-title":"Science"},{"key":"9_CR2","doi-asserted-by":"publisher","first-page":"1454","DOI":"10.3389\/fpsyg.2017.01454","volume":"8","author":"CM Tyng","year":"2017","unstructured":"Tyng, C.M., Amin, H.U., Saad, M.N., Malik, A.S.: The influences of emotion on learning and memory. Front. Psychol. 8, 1454 (2017)","journal-title":"Front. Psychol."},{"key":"9_CR3","doi-asserted-by":"crossref","unstructured":"Jeon, M.: Emotions and affect in human factors and human-computer interaction: Taxonomy, theories, approaches, and methods. In: Emotions and Affect in Human Factors and Human-computer Interaction, pp. 3\u201326 (2017)","DOI":"10.1016\/B978-0-12-801851-4.00001-X"},{"issue":"3","key":"9_CR4","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1080\/13607860410001669750","volume":"8","author":"RS Bucks","year":"2004","unstructured":"Bucks, R.S., Radford, S.A.: Emotion processing in Alzheimer\u2019s disease. Aging Mental Health 8(3), 222\u2013232 (2004)","journal-title":"Aging Mental Health"},{"issue":"2","key":"9_CR5","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1080\/02699930903407948","volume":"24","author":"J Joormann","year":"2010","unstructured":"Joormann, J., Gotlib, I.H.: Emotion regulation in depression: relation to cognitive inhibition. Cogn. Emotion 24(2), 281\u2013298 (2010)","journal-title":"Cogn. Emotion"},{"issue":"3","key":"9_CR6","first-page":"255","volume":"6","author":"W Hu","year":"2020","unstructured":"Hu, W., Huang, G., Li, L., et al.: Video-triggered EEG-emotion public databases and current methods: A survey. Brain 6(3), 255\u2013287 (2020)","journal-title":"Brain"},{"issue":"7","key":"9_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10916-019-1345-y","volume":"43","author":"B Ay","year":"2019","unstructured":"Ay, B., Yildirim, O., Talo, M., Baloglu, U.B., Aydin, G., Puthankattil, S.D., Acharya, U.R.: Automated depression detection using deep representation and sequence learning with EEG signals. J. Med. Syst. 43(7), 1\u201312 (2019)","journal-title":"J. Med. Syst."},{"key":"9_CR8","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1109\/TNSRE.2020.3043426","volume":"29","author":"B Zhang","year":"2020","unstructured":"Zhang, B., Yan, G., Yang, Z., Su, Y., Wang, J., Lei, T.: Brian functional networks based on resting-state EEG data for major depressive disorder analysis and classification. IEEE Trans. Neural Syst. Rehab. Eng. 29, 215\u2013229 (2020)","journal-title":"IEEE Trans. Neural Syst. Rehab. Eng."},{"key":"9_CR9","doi-asserted-by":"crossref","unstructured":"Lee, S.H., Lee, M., Jeong, J.H., Lee, S.W.: Towards an EEG-based intuitive BCI communication system using imagined speech and visual imagery. In: 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), pp. 4409\u20134414 (2019)","DOI":"10.1109\/SMC.2019.8914645"},{"key":"9_CR10","volume-title":"EEG Signal Processing","author":"S Sanei","year":"2013","unstructured":"Sanei, S., Chambers, J.A.: EEG Signal Processing. Wiley, Chichester (2013)"},{"key":"9_CR11","unstructured":"Wu, D., Xu, Y., Lu, B.L.: Transfer learning for EEG-based brain-computer interfaces: A review of progress made since 2016. IEEE Trans. Cogn. Dev. Syst. (2020). Early Access. https:\/\/ieeexplore.ieee.org\/abstract\/document\/9134411"},{"issue":"3","key":"9_CR12","first-page":"255","volume":"6","author":"W Hu","year":"2020","unstructured":"Hu, W., Huang, G., Li, L., et al.: Video-triggered EEG-emotion public databases and current methods: A survey. Brain 6(3), 255\u2013287 (2020)","journal-title":"Brain"},{"key":"9_CR13","doi-asserted-by":"crossref","unstructured":"Comito, C., Forestiero, A., Pizzuti, C.: Word embedding based clustering to detect topics in social media. In: 2019 IEEE\/WIC\/ACM International Conference on Web Intelligence (WI), pp. 192\u2013199 (2019)","DOI":"10.1145\/3350546.3352518"},{"key":"9_CR14","doi-asserted-by":"crossref","unstructured":"Comito, C.: How COVID-19 information spread in US The Role of Twitter as Early Indicator of Epidemics. IEEE Trans. Serv. Comput. (2021) Preprint","DOI":"10.1109\/TSC.2021.3091281"},{"key":"9_CR15","doi-asserted-by":"publisher","first-page":"115831","DOI":"10.1016\/j.image.2020.115831","volume":"84","author":"X Wang","year":"2020","unstructured":"Wang, X., Chen, X., Cao, C.: Human emotion recognition by optimally fusing facial expression and speech feature. Signal Process. Image Commun. 84, 115831 (2020)","journal-title":"Signal Process. Image Commun."},{"key":"9_CR16","doi-asserted-by":"crossref","unstructured":"He, G., Liu, X., Fan, F., You, J.: Image2audio: Facilitating semi-supervised audio emotion recognition with facial expression image. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 912\u2013913 (2020)","DOI":"10.1109\/CVPRW50498.2020.00464"},{"issue":"3","key":"9_CR17","doi-asserted-by":"publisher","first-page":"592","DOI":"10.3390\/s20030592","volume":"20","author":"A Dzedzickis","year":"2020","unstructured":"Dzedzickis, A., Kaklauskas, A., Bucinskas, V.: Human emotion recognition: Review of sensors and methods. Sensors 20(3), 592 (2020)","journal-title":"Sensors"},{"key":"9_CR18","doi-asserted-by":"crossref","unstructured":"Li, W., Huan, W., Hou, B., Tian, Y., Zhang, Z., Song, A.: Can emotion be transferred?-A review on transfer learning for EEG-based emotion recognition. IEEE Trans. Cogn. Dev. Syst. (2021). Early Access. https:\/\/ieeexplore.ieee.org\/document\/9492294","DOI":"10.1109\/TCDS.2021.3098842"},{"issue":"3","key":"9_CR19","first-page":"255","volume":"6","author":"W Hu","year":"2020","unstructured":"Hu, W., Huang, G., Li, L., et al.: Video-triggered EEG-emotion public databases and current methods: A survey. Brain 6(3), 255\u2013287 (2020)","journal-title":"Brain"},{"issue":"1","key":"9_CR20","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1109\/JPROC.2020.3004555","volume":"109","author":"F Zhuang","year":"2020","unstructured":"Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Xiong, H., He, Q.: A comprehensive survey on transfer learning. Proc. IEEE 109(1), 43\u201376 (2020)","journal-title":"Proc. IEEE"},{"issue":"2","key":"9_CR21","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1109\/TNN.2010.2091281","volume":"22","author":"SJ Pan","year":"2010","unstructured":"Pan, S.J., Tsang, I.W., Kwok, J.T., Yang, Q.: Domain adaptation via transfer component analysis. IEEE Trans. Neural Netw. 22(2), 199\u2013210 (2010)","journal-title":"IEEE Trans. Neural Netw."},{"issue":"14","key":"9_CR22","doi-asserted-by":"publisher","first-page":"e49","DOI":"10.1093\/bioinformatics\/btl242","volume":"22","author":"KM Borgwardt","year":"2006","unstructured":"Borgwardt, K.M., et al.: Integrating structured biological data by kernel maximum mean discrepancy. Bioinformatics 22(14), e49\u2013e57 (2006)","journal-title":"Bioinformatics"},{"key":"9_CR23","doi-asserted-by":"crossref","unstructured":"Sun, B., Saenko, K.: Deep coral: Correlation alignment for deep domain adaptation. In: European Conference on Computer Vision, pp. 443\u2013450 (2016)","DOI":"10.1007\/978-3-319-49409-8_35"},{"key":"9_CR24","unstructured":"Zheng, W.L., Lu, B.L.: Personalizing EEG-based affective models with transfer learning. In: Proceedings of the Twenty-fifth International Joint Conference on Artificial Intelligence, pp. 2732\u20132738 (2016)"},{"key":"9_CR25","doi-asserted-by":"crossref","unstructured":"Li, H., et al.: Cross-subject emotion recognition using deep adaptation networks. In: International Conference on Neural Information Processing, Siem Reap, December, pp. 403\u2013413 (2018)","DOI":"10.1007\/978-3-030-04221-9_36"},{"key":"9_CR26","doi-asserted-by":"crossref","unstructured":"Jin, Y.M., et al.: EEG-based emotion recognition using domain adaptation network. In: 2017 International Conference on Orange Technologies (ICOT), pp. 222\u2013225 (2017)","DOI":"10.1109\/ICOT.2017.8336126"},{"key":"9_CR27","unstructured":"Long, M., et al.: Learning transferable features with deep adaptation networks. In: International Conference on Machine Learning, pp. 97\u2013105 (2015)"},{"issue":"5","key":"9_CR28","doi-asserted-by":"publisher","first-page":"1117","DOI":"10.1109\/TNSRE.2020.2985996","volume":"28","author":"W Zhang","year":"2020","unstructured":"Zhang, W., Wu, D.: Manifold embedded knowledge transfer for brain-computer interfaces. IEEE Trans. Neural Syst. Rehab. Eng. 28(5), 1117\u20131127 (2020)","journal-title":"IEEE Trans. Neural Syst. Rehab. Eng."},{"issue":"2","key":"9_CR29","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1109\/TCDS.2019.2949306","volume":"12","author":"J Li","year":"2019","unstructured":"Li, J., Qiu, S., Du, C., Wang, Y., He, H.: Domain adaptation for EEG emotion recognition based on latent representation similarity. IEEE Trans. Cogn. Dev. Syst. 12(2), 344\u2013353 (2019)","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"key":"9_CR30","doi-asserted-by":"crossref","unstructured":"Chen, H., Jin, M., Li, Z., Fan, C., Li, J., He, H.: MS-MDA: multisource marginal distribution adaptation for cross-subject and cross-session EEG emotion recognition. Preprint. arXiv: 2107.07740 (2021)","DOI":"10.3389\/fnins.2021.778488"},{"issue":"7","key":"9_CR31","first-page":"3281","volume":"50","author":"J Li","year":"2019","unstructured":"Li, J., et al.: Multisource transfer learning for cross-subject EEG emotion recognition. IEEE Trans. Cybern. 50(7), 3281\u20133293 (2019)","journal-title":"IEEE Trans. Cybern."},{"key":"9_CR32","doi-asserted-by":"crossref","unstructured":"Zhao, L.M., Yan, X., Lu, B.L.: Plug-and-play domain adaptation for cross-subject EEG-based emotion recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence (2021)","DOI":"10.1609\/aaai.v35i1.16169"},{"key":"9_CR33","doi-asserted-by":"publisher","first-page":"267","DOI":"10.3389\/fnhum.2018.00267","volume":"12","author":"S Liu","year":"2018","unstructured":"Liu, S., et al.: Incorporation of multiple-days information to improve the generalization of EEG-based emotion recognition over time. Front. Hum. Neurosci. 12, 267 (2018)","journal-title":"Front. Hum. Neurosci."},{"key":"9_CR34","doi-asserted-by":"crossref","unstructured":"Hossain, I., Khosravi, A., Hettiarachchi, I., Nahavandi, S.: Multiclass informative instance transfer learning framework for motor imagery-based brain-computer interface. Comput. Intell. Neurosci. (2018). https:\/\/www.hindawi.com\/journals\/cin\/2018\/6323414\/","DOI":"10.1155\/2018\/6323414"},{"issue":"3","key":"9_CR35","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1109\/TAMD.2015.2431497","volume":"7","author":"WL Zheng","year":"2015","unstructured":"Zheng, W.L., Lu, B.L.: Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks. IEEE Trans. Auton. Ment. Dev. 7(3), 162\u2013175 (2015)","journal-title":"IEEE Trans. Auton. Ment. Dev."},{"key":"9_CR36","doi-asserted-by":"crossref","unstructured":"Duan, R.N., Zhu, J.Y., Lu, B.L.: Differential entropy feature for EEG-based emotion classification. In: 2013 6th International IEEE\/EMBS Conference on Neural Engineering (NER), November, pp. 81\u201384 (2013)","DOI":"10.1109\/NER.2013.6695876"},{"issue":"3","key":"9_CR37","doi-asserted-by":"publisher","first-page":"1110","DOI":"10.1109\/TCYB.2018.2797176","volume":"49","author":"WL Zheng","year":"2018","unstructured":"Zheng, W.L., et al.: Emotionmeter: A multimodal framework for recognizing human emotions. IEEE Trans. Cybern. 49(3), 1110\u20131122 (2018)","journal-title":"IEEE Trans. Cybern."},{"issue":"1","key":"9_CR38","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1109\/TAFFC.2015.2436926","volume":"7","author":"M Soleynami","year":"2015","unstructured":"Soleynami, M., Asghari-Esfeden, S., Fu, Y., Pantic, M.: Analysis of EEG signals and facial expressions for continuous emotion detection. IEEE Trans. Affect. Comput. 7(1), 17\u201328 (2015)","journal-title":"IEEE Trans. Affect. Comput."}],"container-title":["Internet of Things","Integrating Artificial Intelligence and IoT for Advanced Health Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-91181-2_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,26]],"date-time":"2023-01-26T12:50:30Z","timestamp":1674737430000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-91181-2_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030911805","9783030911812"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-91181-2_9","relation":{},"ISSN":["2199-1073","2199-1081"],"issn-type":[{"type":"print","value":"2199-1073"},{"type":"electronic","value":"2199-1081"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"24 February 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}