{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T15:06:05Z","timestamp":1777734365456,"version":"3.51.4"},"reference-count":45,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2019,5,17]],"date-time":"2019-05-17T00:00:00Z","timestamp":1558051200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61703277"],"award-info":[{"award-number":["61703277"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanghai Sailing Program","award":["17YF1427000"],"award-info":[{"award-number":["17YF1427000"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Feature selection plays a crucial role in analyzing huge-volume, high-dimensional EEG signals in human-centered automation systems. However, classical feature selection methods pay little attention to transferring cross-subject information for emotions. To perform cross-subject emotion recognition, a classifier able to utilize EEG data to train a general model suitable for different subjects is needed. However, existing methods are imprecise due to the fact that the effective feelings of individuals are personalized. In this work, the cross-subject emotion recognition model on both binary and multi affective states are developed based on the newly designed multiple transferable recursive feature elimination (M-TRFE). M-TRFE manages not only a stricter feature selection of all subjects to discover the most robust features but also a unique subject selection to decide the most trusted subjects for certain emotions. Via a least square support vector machine (LSSVM), the overall multi (joy, peace, anger and depression) classification accuracy of the proposed M-TRFE reaches 0.6513, outperforming all other methods used or referenced in this paper.<\/jats:p>","DOI":"10.3390\/sym11050683","type":"journal-article","created":{"date-parts":[[2019,5,17]],"date-time":"2019-05-17T11:06:46Z","timestamp":1558091206000},"page":"683","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Multiple Transferable Recursive Feature Elimination Technique for Emotion Recognition Based on EEG Signals"],"prefix":"10.3390","volume":"11","author":[{"given":"Jiahui","family":"Cai","sequence":"first","affiliation":[{"name":"School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China"},{"name":"Engineering Research Center of Optical Instrument and System, Ministry of Education, Shanghai Key Lab of Modern Optical System, University of Shanghai for Science and Technology, Shanghai 200093, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China"},{"name":"Engineering Research Center of Optical Instrument and System, Ministry of Education, Shanghai Key Lab of Modern Optical System, University of Shanghai for Science and Technology, Shanghai 200093, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhong","family":"Yin","sequence":"additional","affiliation":[{"name":"School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China"},{"name":"Engineering Research Center of Optical Instrument and System, Ministry of Education, Shanghai Key Lab of Modern Optical System, University of Shanghai for Science and Technology, Shanghai 200093, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,17]]},"reference":[{"key":"ref_1","unstructured":"Panksepp, J. (2005). Affective Neuroscience: The Foundations of Human and Animal Emotions, Oxford University Press, Oxford."},{"key":"ref_2","unstructured":"Schacter, D.L., Gilbert, D.T., Wenger, D.M., and Nock, M.K. (2014). Psychology, Worth. [3rd ed.]."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Siegert, I., B\u00f6ck, R., Vlasenko, B., Philippou-H\u00fcbner, D., and Wendemuth, A. (2011, January 11\u201315). Appropriate emotional labelling of non-acted speech using basic emotions, Geneva emotion wheel and self-assessment manikins. Proceedings of the 2011 IEEE International Conference on Multimedia and Expo, Barcelona, Spain.","DOI":"10.1109\/ICME.2011.6011929"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/0005-7916(94)90063-9","article-title":"Measuring emotion: The self-assessment manikin and the semantic differential","volume":"25","author":"Bradley","year":"1994","journal-title":"J. Behav. Ther. Exp. Psychiatry"},{"key":"ref_5","unstructured":"Parrott, W.G. (2001). Emotions in Social Psychology: Essential Readings, Psychology Press."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ekman, P., Dalgleish, T., and Power, M. (1999). Handbook of Cognition and Emotion, Wiley.","DOI":"10.1002\/0470013494"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1007\/978-3-642-34584-5_11","article-title":"The Hourglass of Emotions","volume":"7403","author":"Cambria","year":"2012","journal-title":"Cogn. Behav. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1007\/BF02686918","article-title":"Pleasure-arousal-dominance: A general framework for describing and measuring individual differences in temperament","volume":"14","author":"Mehrabian","year":"1996","journal-title":"Curr. Psychol."},{"key":"ref_9","unstructured":"Keltner, D., and Ekman, P. (2000). Facial Expression of Emotion, Guilford Publications. [2nd ed.]."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.compind.2017.04.005","article-title":"Respiration-based emotion recognition with deep learning","volume":"92\u201393","author":"Zhang","year":"2017","journal-title":"Comput. Ind."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Tan, D., and Nijholt, A. (2010). Human-Computer Interaction Series, Springer.","DOI":"10.1007\/978-1-84996-272-8"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1016\/j.psychres.2017.05.042","article-title":"Facial emotion recognition and borderline personality pathology","volume":"255","author":"Meehan","year":"2017","journal-title":"Psychiatry Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.neuroimage.2011.07.091","article-title":"The effects of day-to-day variability of physiological data on operator functional state classification","volume":"59","author":"Christensen","year":"2012","journal-title":"Neuroimage"},{"key":"ref_14","unstructured":"Yin, Z., Fei, Z., Yang, C., and Chen, A. (2016, January 24\u201327). A novel SVM-RFE based biomedical data processing approach: Basic and beyond. Proceedings of the IECON 2016\u201442nd Annual Conference of the IEEE Industrial Electronics Society, Firenze, Italy."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1016\/j.rser.2016.11.155","article-title":"A new electricity price prediction strategy using mutual information-based SVM-RFE classification","volume":"70","author":"Shao","year":"2017","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1662","DOI":"10.3389\/fnbot.2017.00019","article-title":"Cross-Subject EEG Feature Selection for Emotion Recognition Using Transfer Recursive Feature Elimination","volume":"11","author":"Yin","year":"2017","journal-title":"Front. Neurorobot."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.neucom.2018.02.073","article-title":"Emotion recognition by assisted learning with convolutional neural networks","volume":"291","author":"He","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.procs.2017.12.003","article-title":"An emotion recognition model based on facial recognition in virtual learning environment","volume":"125","author":"Yang","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1028","DOI":"10.1016\/j.neucom.2017.09.049","article-title":"Efficient and effective strategies for cross-corpus acoustic emotion recognition","volume":"275","author":"Kaya","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Hakanp\u00e4\u00e4, T., Waaramaa, T., and Laukkanen, A.M. (2018). Emotion recognition from singing voices using contemporary commercial music and classical styles. J. Voice.","DOI":"10.1016\/j.jvoice.2018.01.012"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.knosys.2017.10.032","article-title":"An approach to EEG-based gender recognition using entropy measurement methods","volume":"140","author":"Hu","year":"2018","journal-title":"Knowl. Based Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.ijpsycho.2017.04.003","article-title":"The interconnection of mental fatigue and aging: An EEG study","volume":"117","author":"Arnau","year":"2017","journal-title":"Int. J. Psychophysiol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1016\/j.neucom.2017.05.002","article-title":"Cross-subject recognition of operator functional states via EEG and switching deep belief networks with adaptive weights","volume":"260","author":"Yin","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_24","unstructured":"Li, X., Zhang, P., Song, D., Yu, G., Hou, Y., and Hu, B. (2015, January 9\u201313). EEG based emotion identification using unsupervised deep feature learning. Proceedings of the SIGIR2015 Workshop on Neuro-Physiological Methods in IR Research, Santiago, Chile."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Chen, S., Gao, Z., and Wang, S. (2016, January 20\u201325). Emotion recognition from peripheral physiological signals enhanced by EEG. Proceedings of the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China.","DOI":"10.1109\/ICASSP.2016.7472193"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Shahnaz, C., and Hasan, S.M.S. (2016, January 22\u201325). Emotion recognition based on wavelet analysis of Empirical Mode Decomposed EEG signals responsive to music videos. Proceedings of the 2016 IEEE Region 10 Conference (TENCON), Singapore.","DOI":"10.1109\/TENCON.2016.7848034"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wen, Z., Xu, R., and Du, J. (2017, January 15\u201318). A novel convolutional neural networks for emotion recognition based on EEG signal. Proceedings of the 2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC), Shenzhen, China.","DOI":"10.1109\/SPAC.2017.8304360"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Tong, J., Liu, S., Ke, Y.F., Gu, B., He, F., Wan, B., and Ming, D. (2017, January 8\u201310). EEG-based emotion recognition using nonlinear feature. Proceedings of the 2017 IEEE 8th International Conference on Awareness Science and Technology (iCAST), Taichung, China.","DOI":"10.1109\/ICAwST.2017.8256518"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.neulet.2016.09.037","article-title":"An approach to EEG-based emotion recognition using combined feature extraction method","volume":"633","author":"Zhang","year":"2016","journal-title":"Neurosci. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.eswa.2015.10.049","article-title":"Improving BCI-based emotion recognition by combining EEG feature selection and kernel classifiers","volume":"47","author":"Atkinson","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Li, H., Qing, C., Xu, X., and Zhang, T. (2017, January 15\u201318). A novel DE-PCCM feature for EEG-based emotion recognition. Proceedings of the 2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC), Shenzhen, China.","DOI":"10.1109\/SPAC.2017.8304310"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1016\/S1388-2457(00)00527-7","article-title":"The five percent electrode system for high-resolution EEG and ERP measurements","volume":"112","author":"Oostenveld","year":"2001","journal-title":"Clin. Neurophysiol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2102","DOI":"10.1016\/j.eswa.2012.10.013","article-title":"Application of SVM-RFE on EEG signals for detecting the most relevant scalp regions linked to affective valence processing","volume":"40","author":"Santos","year":"2013","journal-title":"Expert Syst. Appl."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.cmpb.2013.09.007","article-title":"Operator functional state classification using least-square support vector machine based recursive feature elimination technique","volume":"113","author":"Yin","year":"2014","journal-title":"Comput. Methods Prog. Biomed."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Hamada, Y., Elbarougy, R., and Akagi, M. (2014, January 9\u201312). A method for emotional speech synthesis based on the position of emotional state in Valence-Activation space. Proceedings of the Signal and Information Processing Association Annual Summit and Conference (APSIPA), Siem Reap, Cambodia.","DOI":"10.1109\/APSIPA.2014.7041729"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/T-AFFC.2011.15","article-title":"DEAP: A database for emotion analysis using physiological signals","volume":"3","author":"Koelstra","year":"2012","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_37","first-page":"205","article-title":"Simple realization of a third order Butterworth filter with MOS-only technique","volume":"81","author":"Atasoyu","year":"2017","journal-title":"AEU"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compbiomed.2017.06.013","article-title":"Simultaneous ocular and muscle artifact removal from EEG data by exploiting diverse statistics","volume":"88","author":"Chen","year":"2017","journal-title":"Comput. Biol. Med."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1109\/THMS.2014.2366914","article-title":"Recognition of mental workload levels under complex human-machine collaboration by using physiological features and adaptive support vector machines","volume":"45","author":"Zhang","year":"2015","journal-title":"Hum. Mach. Syst."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Naser, D.S., and Saha, G. (2013, January 28\u201330). Recognition of emotions induced by music videos using DT-CWPT. Proceedings of the Indian Conference on Medical Informatics and Telemedicine (ICMIT), Kharagpur, India.","DOI":"10.1109\/IndianCMIT.2013.6529408"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Wang, S., and Ji, Q. (2014, January 8\u201312). Emotion recognition from users\u2019 EEG signals with the help of stimulus videos. Proceedings of the 2014 IEEE international conference on multimedia and expo (ICME), Chengdu, China.","DOI":"10.1109\/ICME.2014.6890161"},{"key":"ref_42","first-page":"66","article-title":"Detection of negative emotional states from electroencephalographic (EEG) signals","volume":"8","author":"Feradov","year":"2014","journal-title":"Annu. J. Electron."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Candra, H., Yuwono, M., Handojoseno, A., Chai, R., Su, S., and Nguyen, H.T. (2015, January 25\u201329). Recognizing emotions from EEG subbands using wavelet analysis. Proceedings of the 2015 37th annual international conference of the IEEE engineering in medicine and biology society (EMBC), Milan, Italy.","DOI":"10.1109\/EMBC.2015.7319766"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.eswa.2017.09.062","article-title":"Evolutionary computation algorithms for feature selection of EEG-based emotion recognition using mobile sensors","volume":"93","author":"Nakisa","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2266","DOI":"10.1109\/JSEN.2018.2883497","article-title":"Cross-Subject Emotion Recognition Using Flexible Analytic Wavelet Transform from EEG Signals","volume":"19","author":"Gupta","year":"2019","journal-title":"IEEE Sens. J."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/11\/5\/683\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:53:18Z","timestamp":1760187198000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/11\/5\/683"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,17]]},"references-count":45,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2019,5]]}},"alternative-id":["sym11050683"],"URL":"https:\/\/doi.org\/10.3390\/sym11050683","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,5,17]]}}}