{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T17:09:08Z","timestamp":1779383348773,"version":"3.53.1"},"reference-count":50,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2018,11,11]],"date-time":"2018-11-11T00:00:00Z","timestamp":1541894400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Multichannel physiological datasets are usually nonlinear and separable in the field of emotion recognition. Many researchers have applied linear or partial nonlinear processing in feature reduction and classification, but these applications did not work well. Therefore, this paper proposed a comprehensive nonlinear method to solve this problem. On the one hand, as traditional feature reduction may cause the loss of significant amounts of feature information, Kernel Principal Component Analysis (KPCA) based on radial basis function (RBF) was introduced to map the data into a high-dimensional space, extract the nonlinear information of the features, and then reduce the dimension. This method can provide many features carrying information about the structure in the physiological dataset. On the other hand, considering its advantages of predictive power and feature selection from a large number of features, Gradient Boosting Decision Tree (GBDT) was used as a nonlinear ensemble classifier to improve the recognition accuracy. The comprehensive nonlinear processing method had a great performance on our physiological dataset. Classification accuracy of four emotions in 29 participants achieved 93.42%.<\/jats:p>","DOI":"10.3390\/s18113886","type":"journal-article","created":{"date-parts":[[2018,11,14]],"date-time":"2018-11-14T10:58:22Z","timestamp":1542193102000},"page":"3886","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Emotion Recognition Based on Multichannel Physiological Signals with Comprehensive Nonlinear Processing"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5020-7323","authenticated-orcid":false,"given":"Xingxing","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Intelligence and Computing, Tianjin University, Tianjin 300350, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Intelligence and Computing, Tianjin University, Tianjin 300350, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6031-9334","authenticated-orcid":false,"given":"Wanli","family":"Xue","sequence":"additional","affiliation":[{"name":"College of Intelligence and Computing, Tianjin University, Tianjin 300350, China"},{"name":"School of Computer Science and Engineering, Tianjin University of Technology, Tianjin 300384, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Intelligence and Computing, Tianjin University, Tianjin 300350, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongchuan","family":"He","sequence":"additional","affiliation":[{"name":"Shenzhen Graduate School, Peking University, Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengxin","family":"Gao","sequence":"additional","affiliation":[{"name":"Department of Economics, Pennsylvania State University, State College, PA 16803, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,11,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.clinph.2005.09.009","article-title":"Emotion processing in Parkinson\u2019s disease: Dissociation between early neuronal processing and explicit ratings","volume":"117","author":"Wieser","year":"2006","journal-title":"Clin. Neurophysiol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Tacconi, D., Mayora, O., Lukowicz, P., Arnrich, B., Setz, C., Troster, G., and Haring, C. (2008, January 4\u20136). Activity and emotion recognition to support early diagnosis of psychiatric diseases. Proceedings of the Pervasive Computing Technologies for Healthcare, Hoi An City, Vietnam.","DOI":"10.4108\/ICST.PERVASIVEHEALTH2008.2511"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"718","DOI":"10.1016\/j.psyneuen.2008.02.010","article-title":"What grabs his attention but not hers? Estrogen correlates with neurophysiological measures of vocal change detection","volume":"33","author":"Schirmer","year":"2008","journal-title":"Psychoneuroendocrinology"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"994","DOI":"10.1111\/desc.12262","article-title":"The neural correlates of emotion processing in juvenile offenders","volume":"18","author":"Pincham","year":"2014","journal-title":"Dev. Sci."},{"key":"ref_5","unstructured":"Sun, J.M., Pei, X.S., and Zhou, S.S. (2008, January 12\u201315). Facial emotion recognition in modern distant education system using SVM. Proceedings of the International Conference on Machine Learning and Cybernetics, Kunming, China."},{"key":"ref_6","unstructured":"Gong, M., and Qi, L. (2007, January 18\u201320). Speech emotion recognition in web based education. Proceedings of the 2007 IEEE International Conference on Grey Systems and Intelligent Services, Nanjing, China."},{"key":"ref_7","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_8","doi-asserted-by":"crossref","unstructured":"Li, L., and Chen, J. (2006, January 18\u201320). Emotion Recognition Using Physiological Signals from Multiple Subjects. Proceedings of the 2006 International Conference on Intelligent Information Hiding and Multimedia, Pasadena, CA, USA.","DOI":"10.1109\/IIH-MSP.2006.265016"},{"key":"ref_9","unstructured":"Tsai, J.S., Tsai, J.S., Wang, C.J., and Chung, P.C. (April, January 30). Emotion recognition with consideration of facial expression and physiological signals. Proceedings of the IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, Nashville, TN, USA."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1007\/BF02344719","article-title":"Emotion recognition system using short-term monitoring of physiological signals","volume":"42","author":"Kim","year":"2004","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_11","unstructured":"Cai, J., Liu, G., and Hao, M. (2009, January 25\u201326). The Research on Emotion Recognition from ECG Signal. Proceedings of the 2009 International Conference on Information Technology and Computer Science, Washington, DC, USA."},{"key":"ref_12","first-page":"49","article-title":"Emotion Recognition from Physiological Signals Using Support Vector Machine","volume":"Volume 114","author":"Cheng","year":"2012","journal-title":"Software Engineering and Knowledge Engineering: Theory and Practice"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2230","DOI":"10.1016\/j.compbiomed.2013.10.017","article-title":"EEG-based emotion estimation using Bayesian weighted-log-posterior function and perceptron convergence algorithm","volume":"43","author":"Yoon","year":"2013","journal-title":"Comput. Biol. Med."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1016\/j.neunet.2005.03.004","article-title":"Emotion recognition through facial expression analysis based on a neurofuzzy network","volume":"18","author":"Ioannou","year":"2005","journal-title":"Neural Netw."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"787","DOI":"10.1080\/02699050500110033","article-title":"Recognition of emotion from facial expression following traumatic brain injury","volume":"19","author":"Croker","year":"2005","journal-title":"Brain Inj."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1016\/S0167-6393(03)00099-2","article-title":"Speech emotion recognition using hidden Markov models","volume":"41","author":"Nwe","year":"2003","journal-title":"Speech Commun."},{"key":"ref_17","first-page":"6","article-title":"Adaptive Body Gesture Representation for Automatic Emotion Recognition","volume":"6","author":"Camurri","year":"2016","journal-title":"ACM Trans. Interact. Intell. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Lima, A. (2004, January 1\u20134). On the Use of Kernel PCA for Feature Extraction in Speech Recognition. Proceedings of the European Conference on Speech Communication and Technology, Eurospeech 2003\u2014INTERSPEECH 2003, Geneva, Switzerland.","DOI":"10.21437\/Eurospeech.2003-704"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1109\/21.97458","article-title":"A survey of decision tree classifier methodology","volume":"21","author":"Safavian","year":"2002","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1208","DOI":"10.1126\/science.6612338","article-title":"Autonomic nervous system activity distinguishes among emotions","volume":"221","author":"Ekman","year":"1983","journal-title":"Science"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1016\/j.biopsycho.2010.03.010","article-title":"Autonomic nervous system activity in emotion: A review","volume":"84","author":"Kreibig","year":"2010","journal-title":"Biol. Psychol."},{"key":"ref_22","first-page":"22","article-title":"Social rhythms of the heart","volume":"26","author":"Pantzar","year":"2017","journal-title":"Annu. Rev. Health Soc. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1123\/jsep.22.s1.s122","article-title":"Emotion and motivation: Attention, perception, and action","volume":"22","author":"Lang","year":"2000","journal-title":"J. Sport Exerc. Psychol."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Bradley, M.M., and Lang, P.J. (1999). Measuring emotion: Behavior, feeling, and physiology. Cogn. Neurosci. Emot., 242\u2013276.","DOI":"10.1093\/oso\/9780195118889.003.0011"},{"key":"ref_25","unstructured":"Carlson, N.R. (1994). Physiology of behavior, Allyn and Bacon. [5th ed.]."},{"key":"ref_26","unstructured":"Andreassi, J.L. (2000). Psychophysiology Human Behavior Physiological Response, Psychology Press. [4th ed.]."},{"key":"ref_27","first-page":"36","article-title":"Emotion Recognition Using Bio-sensors: First Steps towards an Automatic System","volume":"3068","author":"Haag","year":"2004","journal-title":"Int. J. Comput. Electr. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.ijhcs.2007.10.011","article-title":"Real-time classification of evoked emotions using facial feature tracking and physiological responses","volume":"66","author":"Bailenson","year":"2008","journal-title":"Int. J. Hum. Comput. Stud."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2067","DOI":"10.1109\/TPAMI.2008.26","article-title":"Emotion Recognition Based on Physiological Changes in Music Listening","volume":"30","author":"Kim","year":"2008","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1109\/TITS.2005.848368","article-title":"Detecting stress during real-world driving tasks using physiological sensors","volume":"6","author":"Healey","year":"2005","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1007\/s10489-010-0241-4","article-title":"Using physiological signals to detect natural interactive behavior","volume":"33","author":"Mohammad","year":"2010","journal-title":"Appl. Intell."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1080\/01449290500331156","article-title":"Using psychophysiological techniques to measure user experience with entertainment technologies","volume":"25","author":"Mandryk","year":"2006","journal-title":"Behav. Inf. Technol."},{"key":"ref_33","unstructured":"Wagner, J., Kim, J., and Andre, E. (2005, January 6\u20138). From Physiological Signals to Emotions: Implementing and Comparing Selected Methods for Feature Extraction and Classification. Proceedings of the 2005 IEEE International Conference on Multimedia and Expo, Amsterdam, The Netherlands."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1016\/j.patcog.2006.07.009","article-title":"Kernel PCA for novelty detection","volume":"40","author":"Hoffmann","year":"2007","journal-title":"Pattern Recognit."},{"key":"ref_35","unstructured":"Mika, S., Smola, A., and Scholz, M. (December, January 29). Kernel PCA and de-noising in feature spaces. Proceedings of the 1998 Conference on Advances in Neural Information Processing Systems II, Denver, CO, USA."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1299","DOI":"10.1162\/089976698300017467","article-title":"Nonlinear component analysis as a kernel eigenvalue problem","volume":"10","author":"Smola","year":"1998","journal-title":"Neural Comput."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/0169-7439(87)80084-9","article-title":"Principal component analysis","volume":"2","author":"Wold","year":"1987","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1007\/s521-001-8051-z","article-title":"Kernel PCA for Feature Extraction and De-Noising in Nonlinear Regression","volume":"10","author":"Rosipal","year":"2001","journal-title":"Neural Comput. Appl."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","article-title":"Extremely randomized trees","volume":"63","author":"Geurts","year":"2006","journal-title":"Mach. Learn."},{"key":"ref_40","unstructured":"Myers, J.L., and Well, A.D. (2003). Research Design and Statistical Analysis, Lawrence Erlbaum Associates Publishers. [2nd ed.]."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/BF00116251","article-title":"Induction on decision tree","volume":"1","author":"Quinlan","year":"1986","journal-title":"Mach. Learn."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"930","DOI":"10.1016\/j.ymssp.2006.05.004","article-title":"Feature selection using Decision Tree and classification through Proximal Support Vector Machine for fault diagnostics of roller bearing","volume":"21","author":"Sugumaran","year":"2007","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/S0167-9473(01)00065-2","article-title":"Stochastic gradient boosting","volume":"38","author":"Friedman","year":"2007","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2001). Boosting and Additive Trees. The Elements of Statistical Learning, Springer.","DOI":"10.1007\/978-0-387-21606-5"},{"key":"ref_46","unstructured":"David, C., and Dianne, D. (2009). The essential 20: Twenty components of an excellent health care team. Pittsburgh: Rose Dog Books, Dorrance Publishing."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1093\/oxfordjournals.eurheartj.a014868","article-title":"Heart rate variability: Standards of measurement, physiological interpretation, and clinical use","volume":"17","author":"Malik","year":"1996","journal-title":"Eur. Heart J."},{"key":"ref_48","unstructured":"Park, B.J., Jang, E.H., Kim, S.H., and Huh, C. (2011, January 6\u20139). Feature selection on multi-physiological signals for emotion recognition. Proceedings of the 2011 IEEE International Conference on Industrial Engineering and Engineering Management, Jeju, Korea."},{"key":"ref_49","first-page":"1","article-title":"Automatic ECG-Based Emotion Recognition in Music Listening","volume":"PP","author":"Hsu","year":"1949","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_50","first-page":"8","article-title":"Analysis of affective ECG signals toward emotion recognition","volume":"27","author":"Xu","year":"2010","journal-title":"J. Electron."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/11\/3886\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:29:08Z","timestamp":1760196548000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/11\/3886"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,11]]},"references-count":50,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2018,11]]}},"alternative-id":["s18113886"],"URL":"https:\/\/doi.org\/10.3390\/s18113886","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,11,11]]}}}