{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T18:46:44Z","timestamp":1767984404812,"version":"3.49.0"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031176173","type":"print"},{"value":"9783031176180","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\/978-3-031-17618-0_8","type":"book-chapter","created":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T03:50:31Z","timestamp":1664596231000},"page":"88-99","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Emotion Recognition from\u00a0Physiological Signals Using Continuous Wavelet Transform and\u00a0Deep Learning"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8878-1969","authenticated-orcid":false,"given":"Lana","family":"Jalal","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2896-9011","authenticated-orcid":false,"given":"Angelika","family":"Peer","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,2]]},"reference":[{"key":"8_CR1","doi-asserted-by":"crossref","unstructured":"Liao, J., Zhong, Q., Zhu, Y., Cai, D.: Multimodal physiological signal emotion recognition based on convolutional recurrent neural network. In: IOP Conference Series: Materials Science and Engineering, pp. 032005. IOP Publishing (2020)","DOI":"10.1088\/1757-899X\/782\/3\/032005"},{"key":"8_CR2","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Cao, X., Lin, J., Yu, D., Cao, X.: Multimodal affective states recognition based on multiscale CNNs and biologically inspired decision fusion model. IEEE Transactions on Affective Computing (2021)","DOI":"10.1109\/TAFFC.2021.3093923"},{"issue":"6","key":"8_CR3","doi-asserted-by":"publisher","first-page":"58","DOI":"10.3390\/brainsci7060058","volume":"7","author":"A Puce","year":"2017","unstructured":"Puce, A., H\u00e4m\u00e4l\u00e4inen, M.S.: A review of issues related to data acquisition and analysis in EEG\/MEG studies. Brain Sci. 7(6), 58 (2017)","journal-title":"Brain Sci."},{"key":"8_CR4","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.inffus.2018.10.009","volume":"51","author":"MM Hassan","year":"2019","unstructured":"Hassan, M.M., Alam, M.G.R., Uddin, M.Z., Huda, S., Almogren, A., Fortino, G.: Human emotion recognition using deep belief network architecture. Inf. Fusion 51, 10\u201318 (2019)","journal-title":"Inf. Fusion"},{"issue":"3","key":"8_CR5","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1016\/0092-6566(77)90037-X","volume":"11","author":"JA Russell","year":"1997","unstructured":"Russell, J.A., Mehrabian, A.: Evidence for a three-factor theory of emotions. J. Res. Pers. 11(3), 273\u2013294 (1997)","journal-title":"J. Res. Pers."},{"key":"8_CR6","doi-asserted-by":"crossref","unstructured":"Soleymani, M., Pantic, M. and Pun, T.: Multimodal emotion recognition in response to videos (Extended abstract). In: 2015 International Conference on Affective Computing and Intelligent Interaction, pp. 491\u2013497 ACII (2015)","DOI":"10.1109\/ACII.2015.7344615"},{"issue":"3","key":"8_CR7","doi-asserted-by":"publisher","first-page":"426","DOI":"10.1037\/0033-2909.110.3.426","volume":"110","author":"JA Russell","year":"1991","unstructured":"Russell, J.A.: Culture and the categorization of emotions. Psychol. Bull. 110(3), 426\u2013450 (1991)","journal-title":"Psychol. Bull."},{"issue":"5","key":"8_CR8","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 18(5), 1383 (2018)","journal-title":"Sensors"},{"key":"8_CR9","doi-asserted-by":"publisher","first-page":"133180","DOI":"10.1109\/ACCESS.2020.3010311","volume":"8","author":"D Wu","year":"2020","unstructured":"Wu, D., Zhang, J., Zhao, Q.: Multimodal fused emotion recognition about expression-EEG interaction and collaboration using deep learning. IEEE Access 8, 133180\u2013133189 (2020)","journal-title":"IEEE Access"},{"issue":"12","key":"8_CR10","doi-asserted-by":"publisher","first-page":"9243","DOI":"10.1016\/j.aej.2022.03.016","volume":"61","author":"RA Alharbey","year":"2022","unstructured":"Alharbey, R.A., Alsubhi, S., Daqrouq, K., Alkhateeb, A.: The continuous wavelet transform using for natural ECG signal arrhythmias detection by statistical parameters. Alex. Eng. J. 61(12), 9243\u20139248 (2022)","journal-title":"Alex. Eng. J."},{"key":"8_CR11","doi-asserted-by":"crossref","unstructured":"Boronoyev, V.V., Garmaev, B.Z., Lebedintseva, I.V.: The features of continuous wavelet transform for physiological pressure signal. In: Fourteenth International Symposium on Atmospheric and Ocean Optics\/Atmospheric Physics, pp. 693611. International Society for Optics and Photonics (2008)","DOI":"10.1117\/12.783357"},{"key":"8_CR12","doi-asserted-by":"crossref","unstructured":"Long, Z., Liu, G., Dai, X.: Extracting emotional features from ECG by using wavelet transform. In: 2010 International Conference on Biomedical Engineering and Computer Science, pp. 1\u20134. IEEE (2010)","DOI":"10.1109\/ICBECS.2010.5462441"},{"key":"8_CR13","doi-asserted-by":"crossref","unstructured":"Cheng, B., Liu, G.: Emotion recognition from surface EMG signal using wavelet transform and neural network. In Proceedings of the 2nd international conference on bioinformatics and biomedical engineering, pp. 1363\u20131366. ICBBE (2008)","DOI":"10.1109\/ICBBE.2008.670"},{"key":"8_CR14","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1016\/j.neuroimage.2013.11.007","volume":"102","author":"GK Verma","year":"2014","unstructured":"Verma, G.K., Tiwary, U.S.: Multimodal fusion framework: a multiresolution approach for emotion classification and recognition from physiological signals. Neuroimage 102, 162\u2013172 (2014)","journal-title":"Neuroimage"},{"key":"8_CR15","doi-asserted-by":"crossref","unstructured":"Ma, J., Tang, H., Zheng, W.L., Lu, B.L.: Emotion recognition using multimodal residual LSTM network. In: Proceedings of the 27th ACM International Conference on Multimedia, pp. 176\u2013183. ACM (2019)","DOI":"10.1145\/3343031.3350871"},{"key":"8_CR16","doi-asserted-by":"crossref","unstructured":"Mei, H., Xu, X.: EEG-based emotion classification using convolutional neural network. In: 2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC), pp. 130\u2013135. IEEE (2017)","DOI":"10.1109\/SPAC.2017.8304263"},{"key":"8_CR17","doi-asserted-by":"crossref","unstructured":"Lin, W., Li, C. and Sun, S.: Deep convolutional neural network for emotion recognition using EEG and peripheral physiological signal. In: International Conference on Image and Graphics, pp. 385\u2013394. Springer, Cham (2017)","DOI":"10.1007\/978-3-319-71589-6_33"},{"key":"8_CR18","doi-asserted-by":"crossref","unstructured":"Liu, N., Fang, Y., Li, L., Hou, L., Yang, F., Guo, Y.: Multiple feature fusion for automatic emotion recognition using EEG signals. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 896\u2013900. IEEE (2018)","DOI":"10.1109\/ICASSP.2018.8462518"},{"key":"8_CR19","doi-asserted-by":"crossref","unstructured":"da Silva, M.A.F., de Carvalho, R.L., da Silva Almeida, T.: Evaluation of a Sliding Window mechanism as DataAugmentation over Emotion Detection on Speech. Acad. J. Comput. Eng. Appl. Math. 2(1), 11\u201318 (2021)","DOI":"10.20873\/uft.2675-3588.2021.v2n1.p11-18"},{"key":"8_CR20","doi-asserted-by":"crossref","unstructured":"Garg, S., Patro, R.K., Behera, S., Tigga, N.P., Pandey, R.: An overlapping sliding window and combined features based emotion recognition system for EEG signals. Appl. Comput. Inform. (2021)","DOI":"10.1108\/ACI-05-2021-0130"},{"issue":"1","key":"8_CR21","doi-asserted-by":"publisher","first-page":"351","DOI":"10.2991\/ijcis.2019.125905651","volume":"12","author":"J Zhou","year":"2019","unstructured":"Zhou, J., Wei, X., Cheng, C., Yang, Q., Li, Q.: Multimodal emotion recognition method based on convolutional auto-encoder. Int. J. Comput. Intell. Syst. 12(1), 351\u2013358 (2019)","journal-title":"Int. J. Comput. Intell. Syst."},{"key":"8_CR22","unstructured":"Karyana, D.N., Wisesty, U.N., Nasri, J.: Klasifikasi sinyal EEG menggunakan deep neural network dengan stacked denoising autoencoder. eProc. Eng. 3(3), 5296\u20135303 (2016)"},{"issue":"1","key":"8_CR23","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/T-AFFC.2011.15","volume":"3","author":"S Koelstra","year":"2012","unstructured":"Koelstra, S., et al.: Deap: a database for emotion analysis; using physiological signals. IEEE Trans. Affect. Comput. 3(1), 18\u201331 (2012)","journal-title":"IEEE Trans. Affect. Comput."},{"key":"8_CR24","series-title":"Smart Innovation, Systems and Technologies","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-319-63859-1_1","volume-title":"Advances in Intelligent Information Hiding and Multimedia Signal Processing","author":"X-Y Zhang","year":"2018","unstructured":"Zhang, X.-Y., Wang, W.-R., Shen, C.-Y., Sun, Y., Huang, L.-X.: Extraction of EEG components based on time - frequency blind source separation. In: Pan, J.-S., Tsai, P.-W., Watada, J., Jain, L.C. (eds.) IIH-MSP 2017. SIST, vol. 82, pp. 3\u201310. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-63859-1_1"},{"key":"8_CR25","doi-asserted-by":"crossref","unstructured":"Sanjar, K., Rehman, A., Paul, A., JeongHong, K.: Weight dropout for preventing neural networks from overfitting. In: Proceedings of the 8th International Conference on Orange Technology (ICOT), pp. 1\u20134. IEEE (2020)","DOI":"10.1109\/ICOT51877.2020.9468799"},{"key":"8_CR26","doi-asserted-by":"publisher","first-page":"7943","DOI":"10.1109\/ACCESS.2021.3049516","volume":"9","author":"Y Zhang","year":"2021","unstructured":"Zhang, Y., Cheng, C., Zhang, Y.: Multimodal emotion recognition using a hierarchical fusion convolutional neural network. IEEE Access 9, 7943\u20137951 (2021)","journal-title":"IEEE Access"},{"key":"8_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"766","DOI":"10.1007\/978-3-319-67585-5_74","volume-title":"Ubiquitous Computing and Ambient Intelligence","author":"A Mart\u00ednez-Rodrigo","year":"2017","unstructured":"Mart\u00ednez-Rodrigo, A., Garc\u00eda-Mart\u00ednez, B., Alcaraz, R., Fern\u00e1ndez-Caballero, A., Gonz\u00e1lez, P.: Study of electroencephalographic signal regularity for automatic emotion recognition. In: Ochoa, S.F., Singh, P., Bravo, J. (eds.) UCAmI 2017. LNCS, vol. 10586, pp. 766\u2013777. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-67585-5_74"},{"key":"8_CR28","doi-asserted-by":"crossref","unstructured":"Bagherzadeh, S., Maghooli, K., Farhadi, J., Zangeneh Soroush, M.: Emotion recognition from physiological signals using parallel stacked autoencoders. Neurophysiology, 50(6), 428\u2013435 (2018)","DOI":"10.1007\/s11062-019-09775-y"},{"key":"8_CR29","doi-asserted-by":"publisher","first-page":"3265","DOI":"10.1109\/ACCESS.2019.2962085","volume":"8","author":"H Huang","year":"2019","unstructured":"Huang, H., Hu, Z., Wang, W., Wu, M.: Multimodal emotion recognition based on ensemble convolutional neural network. IEEE Access 8, 3265\u20133271 (2019)","journal-title":"IEEE Access"}],"container-title":["Lecture Notes in Computer Science","HCI International 2022 - Late Breaking Papers. Multimodality in Advanced Interaction Environments"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-17618-0_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T03:52:45Z","timestamp":1664596365000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-17618-0_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031176173","9783031176180"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-17618-0_8","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":"2 October 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"HCII","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Human-Computer Interaction","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 June 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 July 2022","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":"hcii2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2022.hci.international\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}