{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T06:08:38Z","timestamp":1790834918878,"version":"4.1.0"},"reference-count":41,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,4,15]],"date-time":"2020-04-15T00:00:00Z","timestamp":1586908800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2017YFB1402104"],"award-info":[{"award-number":["2017YFB1402104"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Subject-independent emotion recognition based on physiological signals has become a research hotspot. Previous research has proved that electrodermal activity (EDA) signals are an effective data resource for emotion recognition. Benefiting from their great representation ability, an increasing number of deep neural networks have been applied for emotion recognition, and they can be classified as a Convolutional Neural Network (CNN), a Recurrent Neural Network (RNN), or a combination of these (CNN+RNN). However, there has been no systematic research on the predictive power and configurations of different deep neural networks in this task. In this work, we systematically explore the configurations and performances of three adapted deep neural networks: ResNet, LSTM, and hybrid ResNet-LSTM. Our experiments use the subject-independent method to evaluate the three-class classification on the MAHNOB dataset. The results prove that the CNN model (ResNet) reaches a better accuracy and F1 score than the RNN model (LSTM) and the CNN+RNN model (hybrid ResNet-LSTM). Extensive comparisons also reveal that our three deep neural networks with EDA data outperform previous models with handcraft features on emotion recognition, which proves the great potential of the end-to-end DNN method.<\/jats:p>","DOI":"10.3390\/info11040212","type":"journal-article","created":{"date-parts":[[2020,4,16]],"date-time":"2020-04-16T05:15:41Z","timestamp":1587014141000},"page":"212","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":54,"title":["A Systematic Exploration of Deep Neural Networks for EDA-Based Emotion Recognition"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7697-6382","authenticated-orcid":false,"given":"Dian","family":"Yu","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University, Hangzhou 310037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shouqian","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University, Hangzhou 310037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1175","DOI":"10.1109\/34.954607","article-title":"Toward machine emotional intelligence: Analysis of affective physiological state","volume":"23","author":"Picard","year":"2001","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1016\/j.neunet.2005.03.006","article-title":"Emotion recognition in human\u2013computer interaction","volume":"18","author":"Fragopanagos","year":"2005","journal-title":"Neural Netw."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ko\u0142akowska, A., Landowska, A., Szwoch, M., Szwoch, W., and Wrobel, M.R. (2014). Emotion recognition and its applications. Human-Computer Systems Interaction: Backgrounds and Applications 3, Springer.","DOI":"10.1007\/978-3-319-08491-6_5"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1109\/T-AFFC.2011.25","article-title":"A multimodal database for affect recognition and implicit tagging","volume":"3","author":"Soleymani","year":"2011","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Jerritta, S., Murugappan, M., Nagarajan, R., and Wan, K. (2011, January 4\u20136). Physiological signals based human emotion recognition: A review. Proceedings of the 2011 IEEE 7th International Colloquium on Signal Processing and its Applications, Penang, Malaysia.","DOI":"10.1109\/CSPA.2011.5759912"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"140990","DOI":"10.1109\/ACCESS.2019.2944001","article-title":"A Review, Current Challenges, and Future Possibilities on Emotion Recognition Using Machine Learning and Physiological Signals","volume":"7","author":"Bota","year":"2019","journal-title":"IEEE Access"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1109\/T-AFFC.2012.4","article-title":"Detecting Naturalistic Expressions of Nonbasic Affect Using Physiological Signals","volume":"3","author":"Alzoubi","year":"2012","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kim, K.H., Bang, S.W., and Kim, S.R. (2004). Emotion recognition system using short-term monitoring of physiological signals. Med. Biol. Eng. Comput.","DOI":"10.1007\/BF02344719"},{"key":"ref_9","first-page":"19","article-title":"Emotion Recognition using cvxEDA-Based Features","volume":"10","author":"Ferdinando","year":"2018","journal-title":"J. Telecommun. Electron. Comput. Eng. (JTEC)"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ferdinando, H., and Alasaarela, E. (2018, January 16\u201318). Enhancement of Emotion Recogniton using Feature Fusion and the Neighborhood Components Analysis. Proceedings of the ICPRAM, Funchal, Portugal.","DOI":"10.5220\/0006642904630469"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Machot, F.A., Elmachot, A., Ali, M., Machot, E.A., and Kyamakya, K. (2019). A Deep-Learning Model for Subject-Independent Human Emotion Recognition Using Electrodermal Activity Sensors. Sensors, 19.","DOI":"10.3390\/s19071659"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1109\/TITB.2009.2034649","article-title":"Emotion recognition from EEG using higher order crossings","volume":"14","author":"Petrantonakis","year":"2009","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"627892","DOI":"10.1155\/2014\/627892","article-title":"EEG-based emotion recognition using deep learning network with principal component based covariate shift adaptation","volume":"2014","author":"Jirayucharoensak","year":"2014","journal-title":"Sci. World J."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wan-Hui, W., Yu-Hui, Q., and Guang-Yuan, L. (April, January 31). Electrocardiography recording, feature extraction and classification for emotion recognition. Proceedings of the 2009 WRI World Congress on Computer Science and Information Engineering, Los Angeles, CA, USA.","DOI":"10.1109\/CSIE.2009.130"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ma, C.-w., and Liu, G.-y. (2009, January 6\u20137). Feature extraction, feature selection and classification from electrocardiography to emotions. Proceedings of the 2009 International Conference on Computational Intelligence and Natural Computing, Wuhan, China.","DOI":"10.1109\/CINC.2009.126"},{"key":"ref_16","unstructured":"Venables, P.H., and Christie, M.J. (1980). Electrodermal activity. Tech. Psychophysiol., 54."},{"key":"ref_17","unstructured":"Shukla, J., Barreda-Angeles, M., Oliver, J., Nandi, G., and Puig, D. (2019). Feature Extraction and Selection for Emotion Recognition from Electrodermal Activity. IEEE Trans. Affect. Comput."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1109\/JSEN.2016.2623677","article-title":"Arousal and valence recognition of affective sounds based on electrodermal activity","volume":"17","author":"Greco","year":"2016","journal-title":"IEEE Sensors J."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Placidi, G., Di Giamberardino, P., Petracca, A., Spezialetti, M., and Iacoviello, D. (2016, January 7\u20138). Classification of Emotional Signals from the DEAP dataset. Proceedings of the International Congress on Neurotechnology, Electronics and Informatics, SCITEPRESS, Porto, Portugal.","DOI":"10.5220\/0006043400150021"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., and Fei-Fei, L. (2014, January 23\u201328). Large-scale video classification with convolutional neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.223"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zha, S., Luisier, F., Andrews, W., Srivastava, N., and Salakhutdinov, R. (2015). Exploiting image-trained CNN architectures for unconstrained video classification. arXiv.","DOI":"10.5244\/C.29.60"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Hershey, S., Chaudhuri, S., Ellis, D.P., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., and Seybold, B. (2017, January 5\u20139). CNN architectures for large-scale audio classification. Proceedings of the 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, USA.","DOI":"10.1109\/ICASSP.2017.7952132"},{"key":"ref_23","unstructured":"Lee, J., Kim, T., Park, J., and Nam, J. (2017). Raw waveform-based audio classification using sample-level CNN architectures. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Bartz, C., Herold, T., Yang, H., and Meinel, C. (2017, January 14\u201318). Language identification using deep convolutional recurrent neural networks. Proceedings of the International Conference on Neural Information Processing, Guangzhou, China.","DOI":"10.1007\/978-3-319-70136-3_93"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Fan, Y., Lu, X., Li, D., and Liu, Y. (2016, January 12\u201316). Video-based emotion recognition using CNN-RNN and C3D hybrid networks. Proceedings of the 18th ACM International Conference on Multimodal Interaction, Tokyo, Japan.","DOI":"10.1145\/2993148.2997632"},{"key":"ref_26","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_28","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_29","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems, Curran Associates Inc."},{"key":"ref_30","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012). Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, Curran Associates Inc."},{"key":"ref_31","first-page":"797","article-title":"cvxEDA: A convex optimization approach to electrodermal activity processing","volume":"63","author":"Greco","year":"2015","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1007\/BF00992553","article-title":"A categorized list of emotion definitions, with suggestions for a consensual definition","volume":"5","author":"Kleinginna","year":"1981","journal-title":"Motiv. Emot."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1511\/2001.28.344","article-title":"The nature of emotions: Human emotions have deep evolutionary roots, a fact that may explain their complexity and provide tools for clinical practice","volume":"89","author":"Plutchik","year":"2001","journal-title":"Am. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1161","DOI":"10.1037\/h0077714","article-title":"A circumplex model of affect","volume":"39","author":"Russell","year":"1980","journal-title":"J. Personal. Soc. Psychol."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Greco, A., Valenza, G., and Scilingo, E.P. (2016). Advances in Electrodermal Activity Processing with Applications for Mental Health, Springer.","DOI":"10.1007\/978-3-319-46705-4"},{"key":"ref_36","first-page":"647","article-title":"Decomposition of skin conductance data by means of nonnegative deconvolution","volume":"47","author":"Benedek","year":"2010","journal-title":"Psychophysiology"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.jneumeth.2010.04.028","article-title":"A continuous measure of phasic electrodermal activity","volume":"190","author":"Benedek","year":"2010","journal-title":"J. Neurosci. Methods"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3691","DOI":"10.1152\/jn.01073.2009","article-title":"Fast nonnegative deconvolution for spike train inference from population calcium imaging","volume":"104","author":"Vogelstein","year":"2010","journal-title":"J. Neurophysiol."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Spampinato, C., Palazzo, S., Kavasidis, I., Giordano, D., Souly, N., and Shah, M. (2017, January 21\u201326). Deep learning human mind for automated visual classification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.479"},{"key":"ref_40","unstructured":"Liu, J., Su, Y., and Liu, Y. (2017, January 28\u201329). Multi-modal emotion recognition with temporal-band attention based on lstm-rnn. Proceedings of the Pacific Rim Conference on Multimedia, Harbin, China."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Ferdinando, H., Sepp\u00e4nen, T., and Alasaarela, E. (2017, January 24\u201326). Emotion Recognition Using Neighborhood Components Analysis and ECG\/HRV-Based Features. Proceedings of the International Conference on Pattern Recognition Applications and Methods, Porto, Portugal.","DOI":"10.1007\/978-3-319-93647-5_6"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/11\/4\/212\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:09:10Z","timestamp":1760364550000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/11\/4\/212"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,15]]},"references-count":41,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2020,4]]}},"alternative-id":["info11040212"],"URL":"https:\/\/doi.org\/10.3390\/info11040212","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,4,15]]}}}